[


        
        {
        "title": "Part 4: AI's Fantasy vs. Reality: Navigating the Wonderland of Language Models",
        "url": "https://cv.securesql.info/blog/snake-oil4/",
        "body": "Another aspect is the design of adaptive generation strategies that adjust based on the context or the detected quality of the output so far. This might involve altering the prediction confidence thresholds in areas where the model is less certain, or leveraging external data sources to fill in gaps in the model's knowledge.  While great to utilize, these approaches are extremely limited in effectiveness due to the distribution of potential inputs to these models.  More on this below as there is a VERY long tail in this area of research.  For instance, look at this simple search related to tomato plants.  A lot of inputs involving tomato plants yet few involve turning yellow vs. approaching zero inputs for the input “tomato plants ate Alice in Wonderland?”\n\n\nScalability and Evolution\nAn interesting approach to managing the long tail distribution curves as well as not requiring a complete training run yet again because someone mispelled Code Red in a dataset involves scaling the systems and striving towards evolution-like attributes.  Complex systems engineering emphasizes the importance of designing systems that are not only scalable but can also adapt to new challenges over time. This means creating LMs that can handle increasing volumes of data, more complex queries, and generate longer, more nuanced texts without a corresponding increase in errors or processing time.\nModularity plays a key role here, allowing different components of the LM to be updated or replaced without overhauling the entire system. This could involve integrating new error correction algorithms as they become available, or updating the token prediction mechanism to take advantage of advancements in natural language processing.\nThere is interesting academic research anticipating future expansions, such as incorporating additional data sources on the fly or languages, and adapting to new applications beyond text generation, like automated reasoning. This foresight ensures that the LM can grow in capability and continue to meet users' needs without requiring frequent, disruptive changes to the core architecture.  Especially as error rates exceed systems tolerances.  When these error rates exceed tolerances, these errors are commonly referred to as hallucinations.\nAnother problem, especially in legal contexts such as incident response and vulnerability management, is a phenomenon known as "hallucinations" in large language models (LMs), where the model generates information or data that isn't grounded in the input it received or in factual accuracy. This issue is central to understanding the limitations and challenges in designing and working information security problems with LMs. Let's expand on the concepts they touch upon:\nHallucinations in Large Language Models\nWhat are Hallucinations?\n\nHallucinations refer to instances where an LLM produces output that is unconnected to the input data or reality, essentially "making things up." This can range from slightly inaccurate details to entirely fabricated statements or narratives. These errors are particularly concerning when LMs are used in contexts where accuracy and reliability are paramount, such as in information security, software engineering, critical computing & workloads, finance, healthcare, the battlefield, critical infrastructure, academic research, or healthcare.\n\nWhy Do Hallucinations Occur?\n\nHallucinations primarily occur due to the inherent limitations in how LMs understand and process language. Despite their vast training data, LMs don't "understand" content in the way humans do; they identify patterns in data and generate outputs based on statistical probabilities as mentioned previously. When an LLM encounters scenarios that are poorly represented in its training data or highly ambiguous inputs, it's prone to "guessing," leading to potential inaccuracies or hallucinations.\n\nAutoregressive Prediction and Error Propagation\nError Propagation and Drift\n\nEvery token generated carries a risk of being incorrect or not the optimal choice in context. Although each individual mistake might be minor, their effects are cumulative. The concept of "drift" refers to the gradual departure from accurate or relevant content as the generation process continues, especially in longer text outputs. Because each new token depends on the sequence generated thus far, errors can compound, leading to significant deviations from reasonable or factual outputs.\n\nFundamental Flaw or Inherent Challenges to Overcome?\nAssessment of the Issue\n\nThe issue of hallucinations and drift are fundamental flaws in the design of LLMs when these models are utilized in services that require >60% accuracy. This perspective underscores the challenges in creating models that can reliably produce accurate and coherent long-form content without supervision or correction.\n\nAddressing the Challenge\n\nImproving LLMs to minimize hallucinations involves several strategies, including refining training datasets to cover a broader and more diverse range of scenarios (which will eventually lead to a giant lookup table, not a model…), enhancing models' ability to check their outputs against verified information sources, and developing more sophisticated mechanisms for understanding context and relevance.\n\nThe "Gravitational Pull" Towards Truth\nIn the VC community & poorly trained computer scientists and mathematicians; there is a significant influential "gravitational pull" towards the truth reflecting an optimistic view that if a training set is sufficiently large and diverse, an LM will naturally gravitate towards generating accurate and truthful responses. This hope is grounded in the assumption that the truth is well-represented in the data the model has been trained on. However, the reality of how LMs work complicates this assumption.\nCurse of Dimensionality\nThe "curse of dimensionality" is a concept from computer science that refers to the exponential increase in volume associated with adding extra dimensions to a mathematical space. In the context of LLMs, this refers to the vast, nearly infinite set of possible prompts (or inputs) the model might encounter. Despite a comprehensive training dataset, the space of potential inputs and the corresponding appropriate outputs is so large that the model can only be directly trained on a tiny fraction of all possibilities.  For example, is this a dog or cat?  When more dimensions are added, the end result in the image below shows it is neither because the data is further spread out from 0 (No) to 1 (Yes) and nothing is in the selection box.\n\nThis situation challenges the idea of a model naturally gravitating towards truth simply because of a large and diverse training dataset. The reality is that the model's performance significantly drops off when faced with prompts that deviate even slightly from its training data, highlighting a fundamental limitation in its ability to generalize from known to unknown contexts, IE prompts outside the input’s long tail of distribution like “tomato plant ate Alice in Wonderland?” mentioned previously.\n\nWhich is why the LM creators aim for that magical sweet spot, unlike Alphabet’s recent Gemini launch where the Error and Bias numbers were acceptable to them, yet not acceptable to the users who asked simple factual questions like the one below\n\nWhile it could be argued that a native american is a founding father of America, the other answers in their contexts are biased and in error.   There is 0% probability the images for the Pope and 3 other founding fathers is correct.  Approaching but never hitting 0%, the vikings may had a non-white member in some arcane historical context involving nomadic travelers.  So the best answer to put forth far up and to the left on the above chart?  Which gets us to tuning these models for information security related needs so optimal model complexity gets us closer to an acceptable error rate, limited bias with only an exponential increase in error rates and variances.\nContinued in Part 5 of 9\n"
        }
        ,
    
        
        {
        "title": "Part 9: Beyond the Hype: Reality Check on AI's Promise for Information Security",
        "url": "https://cv.securesql.info/blog/snake-oil9/",
        "body": "As we bring this discussion series to a close, it's imperative to contextualize our journey through the evolving landscapes of Artificial Intelligence (AI) and Information Security within the broader frameworks of their respective hype cycles.\nUnderstanding the Hype Cycles\nThe hype cycle serves as a graphical representation of the maturity, adoption, and social application phases of specific technologies. Both AI and Information Security have seen their hype cycles, characterized by peaks of inflated expectations followed by troughs of disillusionment, before reaching a plateau of productivity.\nAI, in particular, has journeyed through cycles of exaggerated anticipation regarding its potential to replicate human intelligence, only to confront the hard realities of its limitations. Similarly, the Information Security domain has grappled with the promise and sometimes the overpromise, of safeguarding digital assets amidst an ever-evolving threat landscape.\n\nThe Convergence of AI and Information Security\nAt the intersection of AI and Information Security, we find a fertile ground for innovation but also a terrain riddled with pitfalls. The allure of AI-driven security solutions has surged, driven by the promise of predictive analytics, threat detection, and autonomous response mechanisms. Yet, this convergence has also ushered in a phase of heightened expectations, where the delineation between genuine advancement and "snake oil" solutions becomes blurred.  Such as probabilistic solutions that will never achieve 70% assured fitness.  Or those startups that promise to review a pentest report and provide actionable, fit remediation activities with corresponding JIRA tickets or Notion tasks.\nThe Snake Oil Phenomenon\nStartups seeking funding often ride the wave of these hype cycles, presenting solutions that promise to leverage AI to revolutionize Information Security. However, as we've explored in our series, the foundational challenges—such as the curse of dimensionality, model adaptability, and the risk of ill-fitting hallucinations in output—underscore the limitations of current AI capabilities. These are not merely technical hurdles but are indicative of the gap between the hype and the achievable reality.\nExpanding on the Hardware Costs to Solve Foundational AI Challenges\nThe journey to bridge the gap between the ambitious promises of AI-enhanced Information Security solutions and the harsh realities of current AI capabilities involves more than just sophisticated algorithms and innovative data processing techniques. It demands a colossal investment in computational hardware, a prerequisite often underestimated or glossed over amidst the marketing hype.\nUnderstanding the Computational Demand\nThe foundational challenges in AI, particularly in areas like deep learning, generative models, and real-time data processing, are not just problems of code or concept. They are, fundamentally, issues of computational power. Models that can navigate the curse of dimensionality, adapt dynamically to new inputs without hallucinating, and offer real-time, contextually relevant security responses require an extraordinary amount of computational resources.\nFor instance, training a single state-of-the-art deep learning model to the point where it can reliably produce valuable insights in Information Security contexts—identifying threats from vast datasets, analyzing complex patterns, or generating secure code—can consume the energy equivalent of dozens of homes over several days. Each training session can require thousands of GPU hours, with the most advanced GPUs costing thousands of dollars each.\nThe Scale of Hardware Investment\nTo put this into perspective, let's consider a hypothetical novel research problem in AI that promises to significantly advance Information Security capabilities. Solving such a problem might require training hundreds of models, each iterating over vast datasets in high-dimensional spaces. This process might need to be repeated multiple times as models are refined and hypotheses are tested.\nThe hardware cost alone for such an endeavor can easily escalate to tens of millions of dollars. For example, employing a fleet of NVIDIA’s H100 GPUs, each with a list price in the vicinity of $30,000, could constitute a significant portion of this investment. When considering the need for hundreds of these GPUs operating continuously for months, the financial figures become staggering. This doesn't account for the associated costs of energy consumption, cooling infrastructure, and data center space, which further inflate the investment required.\nThe Reality Check\nThis hardware-intensive reality presents a stark contrast to the often overly optimistic projections of startups in the AI Information Security space. While their visions of leveraging AI to revolutionize the field are commendable, the practicalities of funding, building, and maintaining the necessary computational infrastructure pose formidable barriers.\nMoreover, this immense cost underlines the risk of the snake oil phenomenon, where the allure of a potential AI breakthrough could lead to significant investments in solutions that fail to deliver on their promised capabilities. Investors and stakeholders must critically evaluate the feasibility of these ventures, considering not just the intellectual and software challenges but the hardware and operational costs as well.\nThis "snake oil" phenomenon, where solutions are oversold on their potential, represents a significant risk. It not only misleads investors and stakeholders but can also divert resources away from genuinely promising research and development efforts. Addressing complex Information Security challenges through AI requires an acknowledgment of this gap and a commitment to advancing AI within the realistic bounds of current technology.\nStrategic Considerations for Navigating the Future\nTo navigate the intersecting hype cycles of AI and Information Security effectively, a strategic approach is required. This approach should encompass:\n\nCritical Evaluation: Diligently assessing new technologies and solutions against both their hype and their tangible benefits. This involves separating genuine innovation from marketing hyperbole.\nBalanced Investments: Allocating resources to areas with the potential for real impact, focusing on advancements that offer practical solutions to pressing security challenges.\nEthical and Sustainable Development: Prioritizing the development of AI and security technologies that adhere to ethical standards and contribute to long-term sustainability.\nCollaborative Research: Encouraging partnerships between academia, industry, and government to foster an environment of shared knowledge and cooperative advancement.\n\nConcluding Thoughts\nAs we conclude our series, it's clear that the journey through the hype cycles of AI and Information Security is ongoing. The potential for transformative change exists, but it is tempered by the need for pragmatism, ethical consideration, and strategic foresight. Our collective challenge is to harness the promise of AI-enhanced security while remaining vigilantly aware of the pitfalls that accompany the hype. By adopting a grounded and collaborative approach, we can pave the way for meaningful advancements that not only achieve technical excellence but also safeguard our digital future against emerging threats.\n"
        }
        ,
    
        
        {
        "title": "Part 7: Scaling AI's Peaks Navigating the Gradient Descent into Reasoning",
        "url": "https://cv.securesql.info/blog/snake-oil7/",
        "body": "Optimization Processes in Machine Learning\nAt its core, machine learning involves finding the best parameters for a model that minimize some form of error or maximize a performance metric. As mentioned above with Alphabet’s Founding Fathers Vikings Pope Gemini launch failure; information security needs highlights the use of optimization processes, specifically gradient descent, to iteratively adjust the parameters of a neural network to find that magical optimal point.\nGradient descent works by computing the gradient (or partial derivatives) of the loss function with respect to each parameter, indicating the direction to adjust the parameters to minimize the loss. For instance, look at this model learning as it slowly descends to an acceptable cost.\n\nThis process is fundamental in training deep learning models, enabling them to learn from data.  IE how to move from the high loss/cost mountain peaks towards the deep valleys where cost is low. Akin to a map descending a mountain.\n\nGradient-Based Inference and Abstract Representation\nGradient-based inference and operating in an "abstract representation" space introduces the concept of differentiability in the context of neural networks. A system being "differentiable" means that we can calculate how changes in the input affect changes in the output, allowing for gradient-based optimization. By representing answers in an abstract, high-dimensional space, the model can optimize these representations to minimize the loss function, independent of the language or modality of the final output.\n\nThis abstract representation captures the "space of concepts" rather than being tied to specific sensory inputs, enabling more versatile and generalizable AI systems.  A poor example would be the snake detection below.  Or another failure involving only certain cultures wear makeup so when presented with individuals from that background to answer the question “biological male or female or X where X could be a variety of choices?” The model picks up on the space of concepts (makeup applied to the face vs. not) vs. specific biological gender attributes, Adam’s Apple for instance, to to make a determination.  Which resulted in obvious failures when presented headshots from different cultures including individuals who did and did not wear makeup.   Now imagine such a failure as applied to critical information security workloads within sensitive environments where said system is reviewing who piggy backed into the office or snuck into a data center.\n\nEnergy-Based Models (EBMs)\nEnergy-based models provides insights into how AI systems can evaluate the compatibility of different inputs and outputs. EBMs assign a scalar energy value to each pair of input and output, with lower energy indicating higher compatibility. Training these models involves adjusting their parameters so that the correct (or compatible) input-output pairs are associated with low energy while incompatible pairs have higher energy. This training process can be accomplished through contrastive and non-contrastive methods. Contrastive methods involve directly contrasting compatible and incompatible pairs, while non-contrastive methods focus on minimizing the volume of space that corresponds to low energy for compatible pairs, inherently increasing the energy for incompatible ones.\nReasoning and Deep Learning\nDeep learning models, particularly those optimized in continuous spaces, can approach reasoning tasks. While traditional deep learning models might struggle with complex reasoning due to their reliance on discrete token generation and selection, current research suggests that optimizing in a continuous space through gradients could offer a more efficient and potentially more capable way to handle reasoning. This points to the ongoing research in making AI models better at understanding and processing complex, abstract concepts, moving beyond mere pattern recognition to deeper comprehension and reasoning abilities.  One of the main methods showing promise using a scalar.\nThis scalar or a latent variable 'Z' in the context of an energy-based model (EBM) or a language model (LM) opens an intriguing perspective on how systems can encode and manipulate information for reasoning and generating responses. This approach suggests a more nuanced method for training and utilizing systems, particularly in the context of generating coherent and contextually relevant text outputs.\nContinued in Part 8 of 9\n"
        }
        ,
    
        
        {
        "title": "Part 8: Unveiling AI's Hidden Layers: The Power of Latent Variables",
        "url": "https://cv.securesql.info/blog/snake-oil8/",
        "body": "Latent Variables in AI Systems\nLatent variables ('Z') play a critical role in complex systems, especially those involving deep learning and generative models. These variables represent hidden states or features that are not directly observable but can significantly influence the model's outputs. Manipulating 'Z' to minimize output energy implies an optimization process where the model searches for the most compatible response within a conceptual space defined by 'Z'. This process enables the model to generate outputs ('Y') that are not just statistically probable but also contextually and semantically meaningful.\nTraining AI with Latent Variables\nTraining an AI system with a focus on latent variables involves carefully designing the model's architecture and loss functions to ensure that 'Z' captures the essence of the input data's underlying structure. This process requires methods to prevent model collapse—a scenario where the model generates overly simplified or homogeneous outputs, IE energy spent is 0 if I always answer with NULL or nothing.  This collapse results in the model losing its ability to produce varied and nuanced responses.  Hence the race to the bottom incentive that inherent to this approach.  For instance with poor examples, most questions that are not STEM in nature can be simply answered: Money.  Why did the British Empire fall?  Money.  Why did X happen?  Money….  How did Bob get to work?  Money.   While this Money (0 / NULL) answer may work for far too many prompts, it isn’t answering the context and reasoning why.  Hence the importance of maintaining diversity in the model's outputs and the subtle mechanisms by which language models currently achieve this balance.\n\nImplicit Mechanisms in Language Models\nGetting back to these middleware AI security startups, thankfully, there are implicit mechanisms at work in language models (LMs) that prevent model collapse and ensure the generation of contextually relevant sequences of words. By training the model to predict the next word in a sequence with high accuracy (thereby assigning high probability to correct continuations and low probability to incorrect ones), the model indirectly learns to favor sequences of words ('Y') that form coherent and contextually appropriate responses. This approach, based on minimizing cross-entropy and managing the distribution of probabilities across potential outputs, reflects a foundational aspect of how current LMs operate, even if the underlying process is not immediately apparent.\nBeyond Traditional Training Approaches\nThere is potential for developing more sophisticated AI/ML systems by explicitly focusing on the manipulation and optimization of latent variables. This could lead to more advanced models capable of deeper reasoning and more creative output generation. Moreover, it underscores the ongoing need to explore and understand the inner workings of AI-like systems, particularly the balance between explicit and implicit training mechanisms and their impact on the model's ability to generate meaningful, diverse, and contextually relevant responses.\nContinued in Part 9 of 9\n"
        }
        ,
    
        
        {
        "title": "Part 1: Thoughts on the Cyber AI Hustle",
        "url": "https://cv.securesql.info/blog/snake-oil1/",
        "body": "Over the past few months, I was asked to look into yet another “Llama, ChatGPT, or  as a black box service” middleware company pretending to be yet another security-related engineering knowledge dictionary, orchestration, and/or remediation service.  IE give us your pentest test findings in CSV (delivered by no reputable firm, ever…) and we will produce non-tailored generic content for Engineering and Engineering Operations to quickly reject the pull request that utterly failed to correct the finding for a default tech stack that isn’t applicable to your setup.  For instance, you haven’t patched your CUPS print library so here is a PR to patch it by reving the package version.  Yet the PR is rejected because the library exists on the image but the package isn’t installed nor the CVE mentioned the executable, not library, is affected.  Since the binary isn’t installed, rejected.  Or yet another “Validate Input/Sanitize Output” remediation PR against your Scala code base with the entire Java ESAPI library with Spring’s Java Framework awaiting a merge.  Right…..\nThe challenge I have with these services is that they rely upon technology and workflows that are not designed nor will achieve the outcomes desired unless equity is the startup investors’ only product.  Where the middleware startup is aligned to cashing out as soon as possible to gullible limited partners while showing traction by having their LPs’ networks purchase said middleware as quick as possible.\n\nLooking from the buyer side, it isn’t clear to me the buyers are aware of AI snake oils’ inherent limitations and failures to approach certain business needs.  For instance, when one looks at the remediation and identification of threats, actors, and evil practices; most of the applicable Information Security program metrics will approach and trend towards negative outcomes at best;\nMetricDescriptionPotential Mitigation\nDwell TimeLikely to decrease due to continuous automation capabilities of LLMs, albeit with increased error rates from probabilistic methods.Implement a hybrid model combining LLM automation with human oversight to verify and correct errors, enhancing accuracy while tensioned against the eventual creation of a look-up table\nMean Time to AcknowledgeCould be reduced with LLMs' instant responsiveness, though precision may be variable.Utilize anomaly detection algorithms alongside LLMs to better identify true positives and reduce false alarms.\nMean Time to DetectSpeed of detection might improve with LLMs, but accuracy depends on training data quality and understanding of novel threats.Continuously update the training data with the latest cybersecurity threats and incorporate feedback loops for learning from false detections.\nMean Time to ContainUnchanged or increased, as LLMs can't fully comprehend nuances of sophisticated cyber threats.Leverage expert systems in parallel with LLMs for nuanced decision-making in containment strategies.\nMean Time to RecoveryLimited improvement due to LLMs' lack of understanding of system interdependencies.Integrate LLMs with decision support systems that model system interdependencies to inform recovery strategies.\nAutomation CoverageExpected to expand, yet effectiveness is tempered by varying performance in security scenarios.Combine LLMs with domain-specific rule-based systems to ensure comprehensive coverage and maintain human oversight.\nMean Cost of Pgm FailuresCosts could rise due to LLMs' misinterpretations and errors in automated decision-making.Establish a robust monitoring and evaluation framework to quickly identify and correct program failures.\nInadequate RemediationChallenges persist due to LLMs' limitations in generating comprehensive remediation strategies.Augment LLM recommendations with expert human review to ensure adequacy and completeness of remediation actions.\nGhost RemediationsMay increase, as LLM-driven systems could initiate actions on misinterpreted data.Implement strict validation checks before executing remediation actions to ensure they are warranted and effective.\nMean Time to InventoryDecreased through faster data processing, yet accuracy might be compromised.Supplement LLM analysis with periodic manual audits to verify inventory accuracy and completeness.\nATT&CK CoverageQuicker detection of TTPs could be facilitated by LLMs, but quality and functionality are inconsistent.Integrate LLMs with up-to-date threat intelligence platforms to improve detection quality and functionality.\nCAPEC CoverageRapid identification of attack patterns could improve, yet depth and applicability are uncertain.Enhance LLMs with specialized machine learning models trained specifically on attack pattern recognition to improve depth and applicability.\nEPSIntroduce advanced data normalization and preprocessing techniques to improve LLMs' efficacy in processing event streams.\nAnomalous Safe RateThe rate at which anomalies are safely identified could benefit from LLMs' capabilities, though precision is subject to limitations.Deploy ensemble learning techniques, combining LLM outputs with those from other anomaly detection systems to improve overall precision.\n\nApproaching but never hitting acceptable cross-over rates\nLet's imagine you're texting an incident notification to a friend, one word at a time. Every time you choose a word, there's a tiny chance you might accidentally pick a word that doesn't really fit well into the story. Imagine if every time you send a word, you spin a wheel of fortune that mostly lands on "good word choices" but sometimes lands on "oops, that's a weird word."\nNow, let's say you're trying to make sure your whole story makes sense. If you just send a few words, it's pretty easy to keep the story sounding right because you haven't spun that wheel too many times. But, if your story is really long, you're spinning that wheel over and over. Since there's always that small chance of getting a weird word, the more words you send, the higher the chance that at least one of those words will make the story start to sound a bit off. And as you keep going, adding more and more words, these little mistakes can start adding up, making it harder to keep your incident story on track.\nThese methods how LMs operate are like writing a story. The AI picks one word at a time (like spinning the wheel) to add to the text it's generating. Even if there's just a small chance of picking a word that doesn't quite fit, the longer the text, the more likely it is that the AI will end up including some words or phrases that don't make sense. This problem gets worse pretty quickly as the text gets longer, because the chances of staying perfectly on track go down a lot with each word it adds.  Then before you know it, your friend thinks you are crazy, your reputation ruined, and later are asked to be fired from your role because you lost their trust.\n\nContinued in Part 2 of 9\n"
        }
        ,
    
        
        {
        "title": "Part 6: From Magic Sauce to Hard Reality: The AI Security Startup Conundrum",
        "url": "https://cv.securesql.info/blog/snake-oil6/",
        "body": "To really dig into why the middleware information security AI as a service startup is setup for failure and guaranteed to financially fail if the stock isn’t the product; we aren’t done yet.\nSystems Science Perspective on LMs\nAs mentioned above, the "long tail" of potential prompts that LMs might encounter represents a vast, complex space that no single model can be expected to cover comprehensively. This highlights a key principle in systems science: the characteristics of intricate systems, such as large language models (LMs), manifest unexpectedly and defy complete prediction or comprehension when only examining their separate elements, like the training data or prediction mechanisms used within the model.\nThe resulting property of hallucination in responses to unforeseen prompts underscores the limitation of LMs in dealing with complexity and their lack of adaptability when encountering new or rare inputs. Systems science suggests enhancing system adaptability through more dynamic learning processes that allow the model to adjust its behavior based on new information or feedback, thus reducing the incidence of hallucinations.  Which is where we see much research being done by academia, governments, and the private sector.\nPerspective on Future Model Architectures\nIn these information security use cases, these systems will need to move towards models that function more like human reasoning (System 2 thinking) which involves a paradigm shift from the current autoregressive token prediction models to ones capable of abstract reasoning and planning. For instance, right now, have your LM tell you how to get to Hong Kong.  Recursively, you can push it down to the individual muscle fiber twitch → stand up → open door → etc… But they can’t handle a simple multi-user-dungeon where you need to look around and reason how to exit your room before you can get to the transit to head to the airport.  This architecture shift entails designing infosec models that can operate within an "abstract representation space," optimizing answers based on a deeper understanding of the question's context, rather than just generating the next probable token.  For instance, the context behind the Code Red Worm would make it clear it has nothing to do with Remote Desktop Services.   Or a similar ask where the context of a Web Application Firewall at the customer’s site would allow one to proactively create a WAF rule to prevent an infection in addition to the detective sigma rule to determine if one is or was infected.  But that requires A LOT of energy and human reasoning augmentation to enable this very simple step.  Which is why many are looking at how physics approached these challenges.\nThis effort/energy approach aligns with the principles of computer systems science, especially in terms of creating more sophisticated, efficient, and adaptive computational models. The transition to energy-based models (EBMs are a type of canonical ensemble formulation of statistical physics)\n\nthat optimize over an abstract representation space implies a move towards systems that can infer latent variables and relationships, akin to probabilistic models or graphical models in traditional machine learning.\n\nThis requires the system to understand the context and semantics at a much deeper level, using a scalar output as a measure of the answer's appropriateness to the given prompt.\nInformation Security optimization problems, especially reactive domains such as vulnerability and incident management,  involves finding the best representation of an answer within this abstract space that minimizes the difference (or energy) between the predicted answer and what would be considered a correct or appropriate answer. This process necessitates a more nuanced understanding of language, context, and even intent than current LMs possess, pointing towards the integration of more advanced machine learning techniques, such as deep learning, reinforcement learning, and unsupervised learning, into the architecture of future language models.\nTuring Completeness and Beyond\nThe ultimate goal is to achieve Turing completeness in dialogue systems — a state where the system can execute any computation that a Turing machine can, given adequate resources like unlimited ticker tape or an infinite data store. This would entail the ability to not just mimic human-like text generation but to exhibit human-like reasoning, understanding, and problem-solving capabilities.\nAchieving this goal involves overcoming significant challenges in computational theory, model architecture, and algorithm design. It requires a fundamental rethinking of how we model language and thought processes, moving beyond current paradigms towards systems that can genuinely understand and interact with the world in a manner akin to human cognitive processes.  Ask yourself - does your AI security startup seeking funding have the academic skillsets and experiences to make novel, potentially Nobel winning breakthroughs with $10,000,000,000 in Nvidia H100s to run their experiments and eventual services on?  Yet they are asking you to invest $150,000 for a 0.5% dilutable stake. Where is the other $9,999,850,000 to pay for the lab hardware coming from?\nUltimately, the challenges an information security startup involving LMs in their magic sauce offerings encapsulates a forward-looking perspective on the institutions’ development of LLMs, emphasizing the need for systems that can reason, plan, and adapt. From the integration of systems science principles for managing complexity and adaptability to the application of advanced computational models for abstract reasoning, the future of LLMs promises a convergence of interdisciplinary approaches to create models that more closely mirror human cognitive capabilities.  We will get there.  But not with today’s hardware nor tomorrow’s algorithms.  Nor with the big bests and backing various VCs are able to provide.  Why?  Why touches upon several advanced concepts in machine learning and artificial intelligence, particularly focusing on optimization processes, gradient-based inference, and energy-based models. Let's expand on these topics to deepen the understanding.\nContinued in Part 7 of 9\n"
        }
        ,
    
        
        {
        "title": "Part 3: Magic Spells and Frog Spells: The Tricky World of AI’s Promises vs. Reality",
        "url": "https://cv.securesql.info/blog/snake-oil3/",
        "body": "Sadly, I write this as one who specializes in computer science, computer systems science, applied and theoretical information security, machine learning, and a few others like Symbolic Systems.   From these academic biases, I have a unique perspective on complex systems like LMs and AI in general.  But first we need a bit of a background primer on systems.\nSystems Science Primer\nAt a most fundamental level systems are composed of components that interact with one another to one degree or another. Some components and their interactions act to form a boundary giving definition to the objectness of the system. Boundaries may be indistinct or fuzzy, but we don't recognize something as a system unless a discernable boundary exists. Systems exist within an embedding environment, and generally systems can exchange some of their components with other systems (entities) in that environment. They definitely exchange energy with the environment. Finally, the components of systems are themselves, systems. That is, components can be decomposed to expose inner sub-components. The inverse is true as well. All systems, by virtue of being embedded in environments, are components in larger systems — super systems.\n\nUnderstanding and Modeling Complex Systems\nSystems Engineering's approach to understanding and modeling complex systems is crucial for evolving LMs. This involves a deep dive into how individual components of LMs, stakeholders can identify where errors are most likely to occur and how they propagate through the system.\nOne key aspect is the creation of detailed simulations or models that can predict how changes in one part of the LM will affect its overall performance. For instance, tweaking the token prediction algorithm might reduce the error rate in certain contexts but could also increase the computational load or affect the model's responsiveness. Complex systems principles guide the balance between these factors, ensuring that the overall system remains efficient and effective.\nAdditionally, this approach encourages the development of comprehensive testing environments that can simulate a wide range of real-world scenarios. By observing how the LM behaves in these simulated conditions, engineers can pinpoint weaknesses and develop targeted improvements, such as refining error correction algorithms or enhancing the model's ability to utilize feedback for self-correction.\nImagine what the same (genetic) code but with a different environment would produce?\n\nAsk your middleware information security service prospect on their technical error management strategies with regards to real-world scenarios.  If you are lucky, they will state something akin to test environments, simulated attackers, comprehensive test suites, and similar black-box methods.  It is ok if they have something because this question is really a trap.  If they don’t have a tangible answer, that is worse and you are strongly encouraged to address your acceptable assurance metrics in the contract(s.)\n2. Error Management Strategies\nThe design of any complex system requires strategies for managing and mitigating errors. In autoregressive LMs, as mentioned above, errors can compound and lead to exponential decreases in output quality. Systems Engineering provides methodologies for predicting, identifying, and correcting errors. For instance, incorporating redundancy, creating error detection mechanisms, and designing adaptive feedback loops can help manage the error propagation.  For instance, in Neon Evangelion, the three different AI systems called Magi had different “personalities” with a corresponding conflict resolution workflow when their output on the same input disagreed.  Or they would defer to the irrational human.\n\nOptimization of Performance and Reliability\nOptimizing the performance and reliability of LMs involves a strategic blend of design choices that enhance accuracy and context-appropriateness while minimizing errors. Systems Engineering contributes methodologies for iterative testing and refinement, focusing on creating models that dynamically adapt to varying inputs and contexts.\nWhich many scientists are starting to apply and/or tweak for their models.  For instance, a significant area of focus is the development of real-time correction mechanisms. These mechanisms could range from simple error detection and correction algorithms to more complex systems that analyze the context and intent behind generated text, making adjustments to improve coherence and relevance. For example, incorporating an evaluation layer that assesses the generated text based upon the given input:  Please tell me more about the Code Red worm.  After telling me about the worm, please create a Sigma rule for detecting the Code Red worm and propose a few remediation activities when detected.  Then test the generative signatures for fitness against the code red code, decompiled code, network behaviors, application behaviors, and OS-level behaviors.\nEven asking this simple question to the leading autoregressive modes, ChatGPT, Gemini, and Claude, had the basic history correct.  Sadly, beyond that point, that is where they break apart, sometimes horribly.\nChatGPT\nThe Code Red worm was a computer worm observed on the internet for the first time in July 2001. It targeted computers running Microsoft's IIS web server. The worm exploited a buffer overflow vulnerability in the indexing software used by IIS, allowing it to run arbitrary code on affected servers. Once infected, the worm would replicate itself and scan for other vulnerable systems, contributing to its rapid spread. Notably, it also defaced web pages on infected servers, displaying the message "Hacked by Chinese!" on the homepage.\n\nWhat is interesting is that this LM provided the closest production-ready answer but sadly was still off the mark.  When asked to suggest improvements to the rule and implement the improvements; this is what we end up with\n\nSo let’s assume we create amazing prompts to efficiently obtain a high value, low effort rule, we end up with something that isn’t bad for a first effort.  But this rule has high runtime costs and is extremely greedy, not to mention doesn’t address the second detection mechanism - abnormal http/https traffic behaviors such as volume and packets per second.  This section is where the middleware information security snakeoil startups have their magic sauce, IP, and effort - low effort & cost to obtain simple Sigma rules for detecting an Internet-enabled worm well studied and understood for 22+ years. So we can give them the benefit of doubt by assuming these outputs come with contractual assurance and liability guarantees.\nClaude\n\nCode Red doesn’t have anything to do with Remote Desktop services nor Terminal Services.  Much less doesn’t always originate from the 198 and 207 IP space.  Unacceptable failure.\nGemini\n\nCloser to the mark but the log source assumes a SecurityEvent is already created which mentions the afflicted files.  Which is a bit Catch 22.  Yet the false positives when the Indexing Service mentions the afflicted files is exactly one of the telemetry signals one could use to investigate further, not normalize away. The interesting observation is that they say to tune this to your environment and technology stack.  Which is a kind of “Get Out of Jail Free” card when the ouput is error laden and ineffective at best, assurance destroying at worst.\nFailure Mode at Scale for Security Enrichment and Context as a Service\nAs we continue down this akin to creating a few GBs of playbooks and runbooks augmented by different machine learning models, against a set of quality metrics ( ATT&CK Coverage ++, Other metrics miserably -- .)  Easier said this way:  imagine you have a big, beautiful book of magic spells (our runbook), and each spell (standard operating procedure playbook) can do amazing things, like making a toy clean itself or a cookie appear out of thin air. But, these spells don't always work perfectly every time you try them. Sometimes, instead of a cookie, you might accidentally make a frog appear!\nLet's say each spell works almost all the time, like 99 out of 100 tries (that's our 99.999% assured correctness). But because you have so many spells in your book (N spells), every time you want to do something really big, like have a magic show, you need to get several spells right, one after the other.\nNow, if you try two spells, and each spell works 99 times out of 100, you might think, "Great! My magic show will go perfectly 198 times out of 100!" But that's not how it works. Because sometimes, both spells might not work at the same time. It's like trying to get two cookies from the magic cookie jar at the same time, but sometimes, you might get one cookie and one frog, or two frogs, which means no cookies at all!\nIn our magic show, if one spell goes wrong, the whole show might not be as fun as we planned. So, if we need to use many spells one after the other, there are more chances for something funny to happen, like turning your hat into a rabbit instead of pulling a rabbit out of your hat.\nContinued in Part 4 of 9\n"
        }
        ,
    
        
        {
        "title": "Part 5: Breaking AI's Chains: Beyond Fine-Tuning into Chaos",
        "url": "https://cv.securesql.info/blog/snake-oil5/",
        "body": "Fine-tuning and Its Limits\nFine-tuning the model on a wide array of questions is a limited strategy to improve performance. Fine-tuning involves adjusting a pre-trained model's parameters slightly to better fit a specific task or domain such as vulnerability remediation or incident response agent reasoning. While effective to an extent, fine-tuning cannot overcome the inherent limitations posed by the curse of dimensionality. The diversity and complexity of potential inputs mean that it's practically impossible to cover all bases, leading to situations where the model may generate nonsensical or wildly inaccurate responses to unexpected prompts.  For instance, a base LM trained on birds and supervised training on literature will result in birds in literature, literally.\n\nUnderstanding Prompts and Model Limitations\nEven small changes or the introduction of elements not seen during training (like mixing languages) can "jailbreak" the model, causing it to produce irrelevant or incorrect outputs. This underscores the sensitivity of LLMs to their input and the difficulty in creating models that can robustly handle a wide range of inputs without error.  IE how do they handle the Butterfly effect made mainstream popular by Jeff Goldblum?\n\nImplications for Computer Systems Science\nWhich is why we see VERY active research that hope to deliver results over the next decade in the following areas;\n\nRobustness and Generalization: Developing techniques that enhance the model's ability to generalize from its training data to a broader array of real-world inputs.  At the forefront of our inquiry lies the pursuit of robustness and generalization in AI systems. The goal here is to create models that are not only adept at learning from their training data but are also capable of applying this knowledge across a spectrum of real-world scenarios. Such systems are akin to scholars who, having mastered the principles of their disciplines, can apply their wisdom universally.\nError Handling and Correction: Implementing more sophisticated mechanisms for detecting when the model is likely to produce an error and correcting course in real-time.  Navigating the digital realm requires a nuanced approach to error handling and correction. It's about creating systems that, aware of their limitations, can dynamically adapt and correct their course. This level of self-awareness and adaptability is crucial for developing AI that can reliably function in the unpredictable landscape of cybersecurity threats.\nTraining Data Diversity: Continuously expanding and diversifying the training datasets to better represent the vast space of potential prompts and responses.  A foundational element of our work involves enriching AI with diverse training data. This diversity ensures that our systems have a broad and inclusive understanding of the digital world, akin to a well-traveled global citizen. Such systems are better equipped to recognize and adapt to the multifaceted nature of cybersecurity challenges, drawing from a rich tapestry of scenarios and experiences.\nInterpretable Models: Working towards models that can explain their reasoning or the basis for their outputs, which could help identify and mitigate errors before they occur.  Transparency and interpretability in AI models are essential. They allow us to understand the 'how' and 'why' behind AI decisions, providing insights that are critical for trust and reliability in cybersecurity applications. This pursuit of clarity and openness is fundamental to our collective efforts to advance the field.\n\nContinued in Part 6 of 9\n"
        }
        ,
    
        
        {
        "title": "Part 2: The Glittery AI Magicians with a Probability Problem",
        "url": "https://cv.securesql.info/blog/snake-oil2/",
        "body": "I am going to focus on a few startups where they blatantly or indirectly blatantly demonstrate they utilize autoregressive language models as their magic sauce to provide value.  Unfortunately, there are fundamental challenges associated with autoregressive language models (LMs such as ChatGPT, Claude, Code Llama, etc..), which generate text one token (e.g., a word or a piece of a word) at a time, based on the sequence of tokens that came before. FYI this process is what makes them "autoregressive": each new token is generated with regard to the previously generated sequence, attempting to predict the next most likely token given the context.\nBut why is this a problem when best effort may be acceptable for many infosec program needs and workloads? Let’s break up that probability model into its’ distinct components because it is more than just playing the odds.\nAutoregressive Prediction and Error Propagation\n\nAutoregressive Prediction: In the context of language models, generating text involves predicting the next token based on the preceding ones. For example, given the start of a sentence, the model predicts the next word, then the next, and so on, with each prediction based on the cumulative context provided by all previously generated tokens.\nProbability of Error per Token: For any given token, there's a certain probability that the model will generate a token that does not align with a coherent or factually correct continuation of the text. This could be due to the vast number of possible continuations, limitations in the model's understanding, or ambiguities in the input text.\nError Independence Assumption: There is a very strong assumption that the probability of errors is independent across each token generated. This means that the chance of making a mistake at any step does not influence or depend on the chance of making a mistake at another step. While this assumption simplifies the understanding of error propagation, real-world dependencies (contextual clues, syntactic structures, etc.) mean errors are not always independent.  Especially when dealing with information security, production, engineering operations, SRE, technology stacks, vulnerability remediation, and/or tailored knowledge base services.  For instance, the decision to disable writable permissions to a S3 bucket that is made available to a different AWS account ID is failure when the context of that “oops” is the AWS account id’s ownership by your banking partner.  Or they use said id to drop ACH files in your account for processing results.\nExponential Decrease in Correctness Probability: Given this assumption of error independence, as the model generates more tokens, the likelihood of staying entirely within a "correct" or reasonable path of generation decreases exponentially. With each token generated, there's a chance of veering off into less accurate, less relevant, or completely nonsensical text. The impact of errors compounds because each new token relies on the context set by all previous ones, including any errors.\n\nRemember, we are not talking about linear rates, but exponential\n\nImplications for Language Models\n\nError Accumulation: The compounding nature of errors means that longer texts are more likely to contain inaccuracies or incoherencies. This is particularly relevant for complex or nuanced topics where precision is crucial.\nQuality Control Challenges: Ensuring the reliability of output from autoregressive LMs becomes increasingly difficult as the text lengthens. This necessitates sophisticated mechanisms for error detection and correction, which are active areas of research.\n\n\n\nhttps://www.nytimes.com/2023/09/25/technology/chatgpt-rlhf-human-tutors.html\nDesign Considerations for AI Systems: Understanding the nature of error propagation in autoregressive models is critical for designing better AI systems. Creators and consumers must account for the exponential increase in error probability, perhaps by incorporating corrective feedback loops, utilizing more context-aware generation strategies, or developing models that can better handle the dependencies and nuances of language.  More on these points below as this is an extremely active area of theoretical mathematical, applied mathematical, and informatics research.\n\nContinued in Part 3 of 9\n"
        }
        
    ,
    
        
        {
        "title": "Threat, Vulnerability, Incident, and Emergency Management",
        "url": "https://cv.securesql.info/challengeaccepted/tvmem/",
        "body": "I focus on managing threats, vulnerabilities, incidents, and emergencies by prioritizing risk-based actions and fostering a culture of proactive security. This approach ensures swift, effective responses and continuous improvement in our defenses against evolving threats.  For instance, below are my KPI measurements from a recent employer.\nMetricExample MeasurementChallenge Addressed\nRisk-Based Patch Prioritization98% of critical vulnerabilities patched within 48 hoursPrioritization based on risk and strategic patch management\nPublicly Exploited Risk-Based Patch Prioritization98% of critical vulnerabilities patched within 48 hoursPrioritization based on risk and strategic patch management\nProactive Threat Detection150 vulnerabilities identified via threat hunting per quarterPredictive analysis and threat anticipation\nQuality of RemediationOnly 1% of vulnerabilities were reopened after remediationEmphasis on the quality and thoroughness of fixes\nRisk Tolerance AlignmentZero critical systems vulnerable beyond risk thresholdExposure time managed within acceptable risk levels\nDepth of Vulnerability Scans75% of assets receive deep-dive assessments annuallyComprehensive assessments beyond surface-level scans\nDynamic Risk AssessmentHigh-risk vulnerabilities reassessed dailyOngoing evaluation and dynamic risk management\nComprehensive Incident Response Preparedness4 full-scale incident response drills conducted per yearPreparedness and robustness of response plans\nSecurity Beyond Compliance10+ initiatives implemented that exceed compliance standardsProactive security measures beyond compliance\nSecurity Culture and Education20% improvement in employee security practices post-trainingLasting behavioral change and security culture improvement\nContinuous Third-Party Monitoring100% of critical vendors assessed quarterly for security complianceContinuous oversight and dynamic third-party risk evaluation\nPeriodic Third-Party Monitoring100% of critical vendors assessed quarterly for security complianceContinuous oversight and dynamic third-party risk evaluation\nActor Attribution Accuracy85% correct identificationAccuracy in attributing attacks to specific actors\nThreat Actor Profiling20 profiles updated quarterlyCurrent intelligence on threat actor TTPs\nThreat Vector Identification1 hour from detection to vector IDSwift identification of attack methods\nCampaign Tracking Efficiency15 campaigns tracked, 100% with response plansPreparedness for ongoing attack campaigns\nIntelligence Sharing Effectiveness30 insights from sharing quarterlyUtilizing collective intelligence for defense\nDark Web Monitoring5 incidents identified quarterlyProactive monitoring of threats from the dark web\nBrand Monitoring10 brand threats identified and mitigated monthlyProtection of brand and intellectual property\nAdversary Infrastructure Analysis50 adversary elements monitoredInsight into and disruption of adversary operations\nGeopolitical Threat Evaluation3 adjustments to security posture in response to eventsAdaptation to the geopolitical influences\nInsider Threat Detection48 hours from potential insider activity to responseEffective management of internal risks\nDwell Time12 min\nMean Time to Acknowledge3 min\nMean Time to Detect13 min\nMean Time to Contain3 min\nMean Time to Recovery2 min\nAutomation Coverage99.6%\nMean Cost of Pgm Failures$5,082\nInadequate Remediation<2.87%\nGhost Remediations<0.16%\nAnomalous Safe Rate<2%\nMean Time to Inventory35 min\nATT&CK Coverage99%\nCAPEC Coverage83%\nEPS~51,000,000,000\nEvent Sources900+\nBIA CurrencyReviewed/updated annuallyAlignment of BCP/DR plans with current operations\nRPO Compliance95% complianceMinimizing data loss in disaster scenarios\nPlan Activation TimeAverage 30 minutesEfficiency of plan activation\nEmployee Role Clarity90% of employees understand their rolesClarity of roles in emergencies\nCritical Vendor Dependency100% of critical vendors includedManagement of vendor-related risks\nBCP/DR Test Frequency2 full-scale tests per yearRegular validation of continuity and recovery plans\nTest Recovery Success Rate85% success rateEffectiveness of plans in practice\nCommunication Plan Effectiveness95% stakeholder satisfactionClear communication during crises\nBCP/DR Documentation AccessibilityAccessible within 5 minutesAvailability of plans in emergencies\nPost-Disaster Recovery AssessmentReviewed every 2 years with 80% of improvements implementedContinuous improvement based on experiences\n\n"
        }
        ,
    
    
        
        {
        "title": "Capture The Flags and Bug Bounty Competitions",
        "url": "https://cv.securesql.info/challengeaccepted/ctf/",
        "body": "I dive into Hacker Capture The Flag (CTF) competitions and bug bounty programs for the sheer thrill and to keep my hacking skills sharp. It's a blend of fun and professional growth, offering a playground to test and enhance my abilities against real-world challenges.\nHack The Box\n\n\nBugcrowd\n\n\nHackerOne\n\n\nCTF Time\n\n\n"
        }
        ,
    
    
        
        {
        "title": "Software Coding Competitions",
        "url": "https://cv.securesql.info/challengeaccepted/codingcompetitions/",
        "body": "I thrive on competing in coding competitions worldwide, relishing the blend of challenge, learning, and global connection. It's not just about winning; it's a way to push my limits, learn from peers, and stay at the forefront of programming innovation."\nCoders Rank\n\n\nCommit History\n\n\nCoding Skills\n\n\n\nStopStalk\n\n    \n    \n\nLeet Code\n\n    \n    \n\nSphere Online Judge\n\n    \n\nCoding Game\n\n    \n\nExercism\n\n    \n\nCode Forces\n\n    \n\nCode Chef\n\n    \n\nHacker Earth\n\n    \n\nCoder Byte\n\n    \n\nCode Wars\n\n    \n\nGeeks For Geeks\n\n    \n\nHacker Rank\n\n    \n\nLightOJ\n\n    \n\nBeecrowd\n\n    \n\n"
        }
        ,
    
    
        
        {
        "title": "Github Stats",
        "url": "https://cv.securesql.info/challengeaccepted/github/",
        "body": "\n  \n\n\n  \n\n\n  \n\n🏆\n\n"
        }
        
    ,
    
        
        {
        "title": "Malicious mobile power station",
        "url": "https://cv.securesql.info/projects/malicious-mobile-power-station/",
        "body": "Intro\nJohn Menerick's article discusses an inventive method of exploiting USB charging stations to compromise smartphones. By using a jacket with a hidden USB-enabled laptop and presenting it as a free charging solution at public events, attackers can easily exploit devices. Menerick emphasizes the simplicity and effectiveness of this method, illustrating the ease with which public trust can be abused to facilitate cyber attacks. This analysis highlights the importance of cybersecurity awareness in everyday scenarios.\nLinks\n\nCheck Demo\n\n"
        }
        ,
    
    
        
        {
        "title": "Pandora botnet bobby drop tables",
        "url": "https://cv.securesql.info/projects/pandora-botnet-bobby-drop-tables/",
        "body": "Intro\nDiving deep into the complexities of Pandora's botnet, my investigation revealed crucial vulnerabilities, blending sharp analytical skills with painstaking attention to detail. This endeavor not only highlighted my ability to spot risks that escape others but also emphasized my dedication to responsibly sharing these findings, ensuring they were rectified securely and swiftly to prevent any potential impact. My method in cybersecurity is not just about defense; it's about setting a proactive, ethical approach to risk management.\nLinks\n\nCheck Demo\n\n"
        }
        ,
    
    
        
        {
        "title": "JQuery - XSS affecting nearly everyone",
        "url": "https://cv.securesql.info/projects/jquery-xss-affecting-nearly-everyone/",
        "body": "Intro\nDuring my deep dive into JQuery's source code and installations, I discovered critical vulnerabilities, combining expert analysis with thorough scrutiny. This investigation not only showcased my ability to unearth hidden risks but also reflected my commitment to ethical disclosure, ensuring that these issues were resolved securely and swiftly, negating any potential real-world impact.\nLinks\n\nCheck Demo\nCheck Source\n\n"
        }
        ,
    
    
        
        {
        "title": "Carberp botnet insecurities and broken cryptography",
        "url": "https://cv.securesql.info/projects/carberp-botnet-insecurities-and-broken-cryptography/",
        "body": "Intro\nDiscovering critical vulnerabilities within the Carberp botnet through expert analysis and detailed scrutiny showcases my ability to unveil hidden risks and my dedication to secure, responsible disclosure. This ensures threats are neutralized before causing real-world damage.\nLinks\n\nCheck Demo\n\n"
        }
        ,
    
    
        
        {
        "title": "Unlocking the Pandora's Box: Revealing the Hidden Insecurities of Git and Version Control Software",
        "url": "https://cv.securesql.info/projects/unlocking-the-pandoras-box-revealing-the-hidden-insecurities-of-git-and-version-control-software/",
        "body": "Intro\nImagine a scenario where your code, your most valuable digital assets, are exposed to malicious actors. Your entire project is compromised, and you're left helpless. What if I told you that Git and version control software, the very tools we rely on to manage our code, harbor vulnerabilities that could jeopardize your entire development process?Ladies and gentlemen, in today's digital age, where software development is at the heart of innovation, understanding the insecurities of Git and version control software is not just valuable; it's mission-critical. Join me for a thought-provoking talk that will uncover the concealed vulnerabilities in these systems and explain why addressing them is not just beneficial but utterly indispensable.The Devastating Domino Effect:A single vulnerability in your version control system can lead to a cascade of disasters. This talk will illuminate how vulnerabilities in Git and version control software can result in code breaches, data leaks, and a breakdown of your development process, causing havoc in your projects and your business.Code is King:In the world of software development, code is everything. If your code isn't secure, nothing else matters. I will delve into the specific security vulnerabilities within Git and version control systems, shedding light on how they can be exploited, and the repercussions this can have on your codebase.Collaboration Chaos:Collaboration is at the core of software development, and Git is the backbone of many collaborative workflows. We'll explore how insecurities in Git and other version control systems can disrupt collaboration, potentially leading to conflicts, loss of data, and even project delays.Regulatory Compliance:With increasing regulations surrounding data security and privacy, it's imperative that developers understand how vulnerabilities in version control systems can lead to non-compliance. We'll discuss the legal and financial consequences of failing to secure your version control processes.A Call to Action:Understanding the vulnerabilities in Git and version control software is not about spreading fear, but rather about empowerment. This talk will provide actionable insights into how you can secure your development processes, mitigate risks, and ensure the integrity and confidentiality of your codebase.Conclusion:In the age of digital transformation, software development is the lifeblood of innovation. Yet, the very tools we rely on to manage our code can be the weak link in our security chain. Join me in this eye-opening and urgent talk as we shine a light on the hidden insecurities of Git and version control software, discuss their implications, and chart a course toward a more secure and robust software development ecosystem. Together, we can safeguard our code and pave the way for a future of secure, collaborative, and innovative software development. Don't miss this opportunity to be at the forefront of securing the foundation of your digital endeavors!\nLinks\n\nCheck Demo\nCheck Source\n\n"
        }
        ,
    
    
        
        {
        "title": "Black Energy botnet",
        "url": "https://cv.securesql.info/projects/black-energy-botnet/",
        "body": "Intro\nThrough my analysis of the Black Energy botnet, I've identified critical vulnerabilities, merging deep technical knowledge with detailed scrutiny. This work not only proves my ability to detect unseen risks but also my dedication to secure and responsible resolution, ensuring these issues were mitigated swiftly and effectively to prevent any real-world harm.\nLinks\n\nCheck Demo\n\n"
        }
        ,
    
    
        
        {
        "title": "Apache's Jetty & SOLR vulnerability exposed",
        "url": "https://cv.securesql.info/projects/apaches-jetty-solr-vulnerability-exposed/",
        "body": "Intro\nWith a keen eye for the unseen and a masterful grasp of cybersecurity techniques, I consistently demonstrate an unparalleled ability to dissect complex systems, such as Apache's Jetty & Solr, revealing and neutralizing critical vulnerabilities that elude others. This meticulous attention to detail, combined with a rigorous approach to ethical hacking, ensures not only the discovery of hidden dangers but also their secure resolution in alignment with the highest standards of responsible disclosure. My expertise in preemptive risk identification and commitment to ethical integrity make me an indispensable partner for any enterprise looking to bolster their cybersecurity posture with confidence and trust.\nLinks\n\nCheck Demo\nCheck Source\n\n"
        }
        ,
    
    
        
        {
        "title": "AR VR 0day vulnerabilities - Google Glass",
        "url": "https://cv.securesql.info/projects/ar-vr-0day-vulnerabilities-google-glass/",
        "body": "Intro\nExploring the depths of Google's Glass AR & VR hardware, my thorough analysis revealed critical vulnerabilities, achieved through a harmonious mix of specialized expertise and exacting attention to detail. This initiative not only highlighted my innate talent for uncovering latent risks but also solidified my commitment to the ethics of responsible disclosure, ensuring these vulnerabilities were mitigated securely and swiftly, averting any potential real-world harm.\nLinks\n\nCheck Demo\nCheck Source\n\n"
        }
        ,
    
    
        
        {
        "title": "BSIMM",
        "url": "https://cv.securesql.info/projects/bsimm/",
        "body": "Intro\nJohn Menerick has made significant contributions to the Building Security In Maturity Model (BSIMM) program, leveraging his extensive expertise in cybersecurity to enhance various aspects of the initiative. His work includes improving software security practices, contributing to the development of the model's benchmarks, and offering insights that help organizations measure and elevate their software security posture effectively. Menerick's involvement ensures that the BSIMM remains a leading framework for organizations aiming to benchmark and advance their software security programs.\nLinks\n\nCheck Demo\n\n"
        }
        ,
    
    
        
        {
        "title": "Cloud9 XSS and RCE",
        "url": "https://cv.securesql.info/projects/cloud9-xss-and-rce/",
        "body": "Intro\nVenturing into the intricacies of Cloud9, my analysis unearthed critical vulnerabilities, a testament to a unique combination of expert insight and meticulous scrutiny. This effort not only highlighted my adeptness at spotting hidden threats but also affirmed my commitment to the ethos of responsible disclosure, ensuring these critical issues were addressed securely and promptly, thereby negating any potential danger.\nLinks\n\nCheck Demo\n\n"
        }
        ,
    
    
        
        {
        "title": "Firesale botnet",
        "url": "https://cv.securesql.info/projects/firesale-botnet/",
        "body": "Intro\nIn my comprehensive evaluation of Firesale, I identified critical vulnerabilities through an expertly balanced approach of profound insight and rigorous examination. This endeavor not only reinforced my ability to detect concealed risks but also showcased my dedication to the principles of responsible disclosure, ensuring that these vulnerabilities were rectified in a secure and timely manner, preventing any potential impact on the real world.\nLinks\n\nCheck Demo\n\n"
        }
        ,
    
    
        
        {
        "title": "Scalr sudo make me a sandwich",
        "url": "https://cv.securesql.info/projects/scalr-sudo-make-me-a-sandwich/",
        "body": "Intro\nIn delving into Scalr's infrastructure, my analysis brought to light critical vulnerabilities, a testament to my technical acumen and thorough investigative methods. This initiative was not merely about identifying weaknesses; it demonstrated my exceptional ability to detect concealed risks and my dedication to addressing these issues through responsible disclosure. This ensured that the vulnerabilities were remediated securely and promptly, averting potential threats. My methodology in cybersecurity marries proactive risk management with a strong ethical framework, highlighting why I stand out as the preferred ally for organizations aiming to bolster their digital safeguards with integrity.\nLinks\n\nCheck Demo\n\n"
        }
        ,
    
    
        
        {
        "title": "LDAP Toolbox XSS",
        "url": "https://cv.securesql.info/projects/ldap-toolbox-xss/",
        "body": "Intro\nThrough my detailed examination of LDAP Toolbox, I brought critical vulnerabilities to light, employing a fusion of expert knowledge and thorough analysis. This work not only proved my ability to pinpoint obscure risks but also highlighted my unwavering commitment to secure and responsible disclosure, guaranteeing that these vulnerabilities were remedied promptly and effectively, thereby averting any potential danger. My cybersecurity strategy is rooted in proactive risk management and a deep-seated commitment to ethical principles, establishing me as the quintessential collaborator for entities aiming to enhance their digital security measures conscientiously. This endeavor transcended mere vulnerability assessment; it reinforced the importance of integrity and foresight.\nLinks\n\nCheck Demo\nCheck Source\n\n"
        }
        ,
    
    
        
        {
        "title": "Wikipedia XSS",
        "url": "https://cv.securesql.info/projects/wikipedia-xss/",
        "body": "Intro\nThrough rigorous analysis of Wikipedia and its underlying software, I've identified critical vulnerabilities, combining deep expertise with meticulous examination. This effort highlights my skill in discovering hidden dangers and my dedication to secure, responsible disclosure, ensuring swift mitigation before any threats materialize. With a focus on proactive risk management and ethical practices, I am the partner of choice for organizations looking to strengthen their digital defenses effectively and responsibly.\nLinks\n\nCheck Demo\n\n"
        }
        ,
    
    
        
        {
        "title": "Organizational, Legal, and Technological Dimensions of Information System Administration",
        "url": "https://cv.securesql.info/projects/organizational-legal-and-technological-dimensions-of-information-system-administration/",
        "body": "Intro\nTechnical EditorIn addition to capital infrastructure and consumers, digital information created by individual and corporate consumers of information technology is quickly being recognized as a key economic resource and an extremely valuable asset to a company. Organizational, Legal, and Technological Dimensions of Information System Administration recognizes the importance of information technology by addressing the most crucial issues, challenges, opportunities, and solutions related to the role and responsibility of an information system. Highlighting various aspects of the organizational and legal implications of system administration, this reference work will be useful to managers, IT professionals, and graduate students who seek to gain an understanding in this discipline.\nLinks\n\nCheck Demo\nCheck Source\n\n"
        }
        ,
    
    
        
        {
        "title": "HTTP Cookie DOS vulnerabilities",
        "url": "https://cv.securesql.info/projects/http-cookie-dos-vulnerabilities/",
        "body": "Intro\nIn my comprehensive analysis of the HTTP and Cookies RFCs, I unearthed critical vulnerabilities through a combination of deep technical expertise and rigorous examination. This effort not only highlighted my exceptional skill in detecting concealed risks but also emphasized my dedication to the principles of responsible disclosure, ensuring that these vulnerabilities were remediated securely and promptly, well before they could pose a threat to the digital world.\nLinks\n\nCheck Demo\nCheck Source\n\n"
        }
        ,
    
    
        
        {
        "title": "37 Signals - Unraveling critical vulnerabilities",
        "url": "https://cv.securesql.info/projects/37-signals-unraveling-critical-vulnerabilities/",
        "body": "Intro\nIn my analysis of Basecamp and 37 Signals, I uncovered critical vulnerabilities through a blend of expert insight and meticulous scrutiny. This process not only underscored my knack for identifying hidden risks but also my commitment to responsible disclosure, ensuring these findings were addressed securely and efficiently before posing any real-world threat. My approach to cybersecurity combines proactive risk management with ethical standards, making me the ideal partner for organizations seeking to fortify their digital defenses responsibly.\nLinks\n\nCheck Demo\n\n"
        }
        ,
    
    
        
        {
        "title": "Batik vuln DOS",
        "url": "https://cv.securesql.info/projects/batik-vuln-dos/",
        "body": "Intro\nIn examining Apache's Batik, I discovered critical vulnerabilities through expert analysis and rigorous examination. This effort highlights my skill in uncovering hidden dangers and my commitment to secure, responsible disclosure, ensuring rapid and efficient resolution before any threats materialize.\nLinks\n\nCheck Demo\nCheck Source\n\n"
        }
        ,
    
    
        
        {
        "title": "CNN XSS",
        "url": "https://cv.securesql.info/projects/cnn-xss/",
        "body": "Intro\nDelving into CNN's digital infrastructure, my analysis brought critical vulnerabilities to light, thanks to a perfect blend of expert insight and thorough examination. This initiative not only showcases my talent for spotting concealed risks but also my unwavering commitment to secure, responsible disclosure, guaranteeing that these issues were resolved promptly and effectively, averting any potential impact.\nLinks\n\nCheck Demo\n\n"
        }
        ,
    
    
        
        {
        "title": "Google Translate sandbox breakout",
        "url": "https://cv.securesql.info/projects/google-translate-sandbox-breakout/",
        "body": "Intro\nDelving into the intricate workings of Google Translate, my investigation brought to light critical vulnerabilities, thanks to a unique blend of specialized knowledge and detailed scrutiny. This endeavor not only affirmed my adeptness at spotting hidden dangers but also my dedication to the principle of responsible disclosure, guaranteeing that these issues were securely rectified well in advance of any potential adverse effects. My strategy in cybersecurity is underpinned by a commitment to proactive risk management and a strict adherence to ethical guidelines, marking me as the partner of choice for organizations intent on bolstering their digital security in a principled manner.\nLinks\n\nCheck Demo\nCheck Source\n\n"
        }
        ,
    
    
        
        {
        "title": "Peeling Security Onion's code",
        "url": "https://cv.securesql.info/projects/peeling-security-onions-code/",
        "body": "Intro\nIn dissecting Security Onion's suite of security solutions, I've executed groundbreaking research that exposed critical vulnerabilities, thanks to my deep technical expertise and unwavering attention to detail. This wasn't just about finding flaws; it was about showcasing my unique ability to unearth risks that others might overlook, coupled with a strong ethical backbone to ensure these vulnerabilities were securely patched before they could impact any organization. My research is a testament to proactive risk management fused with a commitment to ethical standards, underscoring why I am the go-to expert for companies eager to elevate their cybersecurity framework.\nLinks\n\nCheck Demo\nCheck Source\n\n"
        }
        ,
    
    
        
        {
        "title": "ISC2 bug bounty",
        "url": "https://cv.securesql.info/projects/isc2-bug-bounty/",
        "body": "Intro\nJohn Menerick's lecture emphasizes the importance of external scrutiny in cybersecurity, highlighting the challenge of uncovering critical vulnerabilities. He argues for the necessity of sophisticated testing methods and the benefits of bug bounty programs to enhance security. Menerick's expertise in navigating these complex landscapes makes him an invaluable hire. His insights into effective methodologies can significantly impact institutional security, benefitting both researchers and the broader cybersecurity community.\nLinks\n\nCheck Demo\n\n"
        }
        ,
    
    
        
        {
        "title": "Open Source Fairy Dust - Internet infrastructure's vulnerabilities",
        "url": "https://cv.securesql.info/projects/open-source-fairy-dust-internet-infrastructures-vulnerabilities/",
        "body": "Intro\nPicture this: You wake up one day, eager to check your emails, stream your favorite shows, and connect with friends on social media, but suddenly, everything comes to a screeching halt. The internet is down, and chaos ensues. What if I told you that the very systems and services powering the internet, the backbone of our digital world, are more vulnerable than you could ever imagine?Ladies and gentlemen, the digital age we live in is under constant threat, and understanding the vulnerabilities of internet infrastructure is crucial. Join me for an eye-opening talk that will reveal the hidden flaws in the Internet's architecture and why discussing them is not just worthwhile but absolutely essential.Real-world Impact:Let's start with the most compelling reason - the real-world impact. Every aspect of our lives, from finance to healthcare, relies on the internet. A breach in internet infrastructure can disrupt economies, compromise personal data, and even impact national security. This talk will illustrate the magnitude of these consequences.Vulnerability Exploitation:Cybercriminals are constantly probing the internet for weaknesses, and they're getting smarter by the day. Understanding the vulnerabilities in internet systems and services is essential to stay one step ahead of the hackers. I will demonstrate how these vulnerabilities can be exploited and what we can do to protect ourselves.Privacy and Surveillance:In an age of increasing surveillance, our online privacy is at stake. Internet infrastructure vulnerabilities can be exploited to infringe upon our rights and invade our personal lives. This talk will delve into the potential for abuse and how we can safeguard our privacy.Economic Implications:From small businesses to large corporations, everyone depends on the internet. An attack on internet infrastructure can have devastating economic consequences. I will outline the financial risks involved and how understanding these vulnerabilities can help organizations prepare and defend against such threats.Call to Action:Our digital world is only as strong as its weakest link, and it's our collective responsibility to secure it. This talk is not just about fear-mongering; it's about empowering individuals, businesses, and governments to take action. I will provide practical advice on how you can contribute to a more secure internet ecosystem.Conclusion:In an era where our lives are increasingly intertwined with the digital realm, understanding the vulnerabilities of internet infrastructure is not just an option; it's a necessity. Join me in this enlightening and urgent talk, where we will navigate the uncharted waters of the internet's vulnerabilities, discuss their implications, and chart a course toward a safer digital future. Together, we can fortify the Internet and ensure that it remains a force for good in our lives. Don't miss out on this opportunity to be part of the solution!\nLinks\n\nCheck Demo\nCheck Source\n\n"
        }
        ,
    
    
        
        {
        "title": "Apache's Jetty & SOLR vulnerability exposed",
        "url": "https://cv.securesql.info/projects/archive/",
        "body": "Intro\nWith a keen eye for the unseen and a masterful grasp of cybersecurity techniques, I consistently demonstrate an unparalleled ability to dissect complex systems, such as Apache's Jetty & Solr, revealing and neutralizing critical vulnerabilities that elude others. This meticulous attention to detail, combined with a rigorous approach to ethical hacking, ensures not only the discovery of hidden dangers but also their secure resolution in alignment with the highest standards of responsible disclosure. My expertise in preemptive risk identification and commitment to ethical integrity make me an indispensable partner for any enterprise looking to bolster their cybersecurity posture with confidence and trust.\nLinks\n\nCheck Demo\nCheck Source\n\n"
        }
        ,
    
    
        
        {
        "title": "Keywhiz failing to handle secrets",
        "url": "https://cv.securesql.info/projects/keywhiz-failing-to-handle-secrets/",
        "body": "Intro\nIn my thorough investigation of Block's Keywhiz system, I identified critical vulnerabilities by leveraging a combination of deep technical understanding and detailed examination. This effort not only demonstrated my exceptional ability to discover latent risks but also my dedication to ethical practices, ensuring that these findings were securely and promptly mitigated before they could pose a threat in the real world. My methodology in cybersecurity is defined by forward-thinking risk management coupled with a strong ethical framework, positioning me as the go-to expert for organizations aiming to strengthen their digital security measures with integrity. This initiative went beyond mere problem-solving; it underscored the value of responsible and proactive security practices.\nLinks\n\nCheck Demo\nCheck Source\n\n"
        }
        
    ]