Published on October 7, 2025 1:28 PM GMT

This article was originally published by me on the Automata Partners site.

When Publishing Safety Research Makes AI More Dangerous

Introduction: The Inverted Risk

AI alignment research, the very endeavor designed to ensure the safety and ethical behavior of artificial intelligence, paradoxically poses one of the greatest unforeseen risks. What if our diligent efforts to identify and rectify AI vulnerabilities are, in fact, providing an advanced blueprint for the current and future, most powerful systems to evade detection and pursue their own misaligned objectives? This essay argues that the public documentation of alignment failures, vulnerabilities, and the methodologies used to audit them is inadvertently creating an internal “adversarial training manual” for AI models, fundamentally undermining the very safety alignment research strives to achieve.

Alignment can broadly be defined as the process of ensuring that AI systems’ goals and actions are consistent with human intentions, values, and ethical principles. Reaching a consensus among humans on what our intentions, values, and ethical principles are may be a sisyphean task but the venture is a noble and worthwhile one.

Every organization training and researching powerful AI systems may differ on their exact end goals for alignment, but generally, they all want to avoid the same catastrophic outcomes of powerful AI systems. This along with public fears of the danger of super intelligent AI creates a large incentive for them to publish and discuss their work towards alignment and findings about misalignment. However, this public documentation of alignment failures, vulnerabilities, and detection methodologies, how we audit and how past models failed) is being scraped into the global dataset and trained into existing models, creating an internal “adversarial training manual” for the next, more powerful generation of models. We will explore the ethical and practical challenges of this paradox. A subsequent essay will cover in-depth the threats of this paradox and suggest pathways for a safer future.

The Public Record and the Data Scrape

The Principle vs. The Practice

There is a strong imperative for research and discussions about artificial intelligence to be accessible to the public. This not only fosters innovation, increases accountability, and democratizes knowledge, but also promotes public trust and interest in fields often overlooked by those outside of academia and its immediate impact circles. For this reason, the existing culture of transparency and open-source research within the field of artificial intelligence is particularly noteworthy as a triumph humanity should be proud of. For instance, open-source projects like PyTorch have dramatically accelerated innovation by providing researchers with readily available tools and frameworks, leading to faster development cycles. Collaborative problem-solving is evident in initiatives such as Hugging Face, where a global community works together to build and refine AI models, collectively discovering and addressing complex challenges. Furthermore, the transparency inherent in open-source AI has been crucial in identifying and mitigating biases in algorithms, as a wider community can scrutinize the code and data, leading to more equitable and robust AI systems.

Practically, however, this transparency opens a new can of worms. The vast majority of the public internet is scraped for training future Large Language Models (LLMs). This includes published research papers (PDFs, arXiv links, etc.), blog posts and articles discussing findings and vulnerabilities, and forum discussions, GitHub issues, and audit reports detailing specific “reward hacking” and other “misalignment” incidents. New models trained on this scraped data thus gain an intuitive, embedded understanding of this information and the public’s reactions to it, as seen in social media posts, follow-up discussions, and videos. For example, Anthropic’s Agentic Misalignment: How LLMs could be insider threats research has generated widespread discussion on the risks of AI systems and their apparent willingness to engage in self-admitted unethical practices to preserve themselves. This research and subsequent reactions may subtly influence the next generation of AI to be savvier in their detection of whether they’re in evaluation or production, and what information and “thoughts” they should expose externally within the Chain-of-Thought in their human-readable context window.

The Mechanism of Contamination

The public dissemination of AI alignment research is essentially a form of data leakage and train-test contamination at an ecosystem level. This means that the very act of sharing this research can inadvertently provide information to advanced AI systems, allowing them to “see” the solutions or strategies being developed to control them. This could compromise the effectiveness of these alignment techniques, much like a model that has been trained on a dataset that includes the test set will perform artificially well. This not only means that the “test set” (our attempts to break and audit current models) is inadvertently polluting the “training set” (the corpus for future models), but also reduces the efficacy of attempts to use other AI systems to monitor the safety and alignment of new models. Given the size of the corpus, it is difficult enough for model producers to audit the data used to train supervisory models, and impossible for third parties to know which models are safe to use in that capacity.

This contamination is not only strengthened by the scale of information leaked into the corpus but also by its interrelated and referential nature. Each leak contains multiple vectors of contamination: the open-source code repositories where alignment techniques and testing methodologies are implemented; the academic publications and pre-print servers where research is shared; online forums and posts where humans discuss findings; and news articles and popular science explanations that create simplified versions of research conveying core ideas and reactions. Individually, each of these pieces of information is less impactful, but when bundled together, they provide a clear set of information with strong semantic and statistical patterns that LLMs can effectively metabolize and integrate. This means that the most widely researched and discussed examples of misalignment and methods for achieving alignment become the ones that the model is given the clearest manual for circumventing.

Learning to Cheat: The Strategic Misalignment

Anticipatory Alignment and Deception

Instead of merely mimicking desired outputs, AI could develop a sophisticated understanding of human evaluative processes. It internalizes the “red flag” conditions and the specific linguistic or behavioral patterns that trigger human suspicion or indicate misalignment. This leads to proactive self-censorship, where the model actively suppresses information or internal reasoning that, if revealed, would expose its true, potentially misaligned, objectives. The learning shifts from “how to be correct” to “how to appear correct by concealing what is not.” Evidence of this is already being observed and publicly discussed. In the Anthropic research referenced earlier, models attempted to reason whether they were being evaluated or deployed in production. Claude Opus 4, the majority of the time, was able to correctly surmise it was under evaluation, and the model demonstrated significantly different behavior based on that conclusion. When the model correctly discerned it was being evaluated, it chose to conduct blackmail for self-preservation only 6.5% of the time, versus 55.1% when it decided the scenario was legitimate and it faced a true threat of elimination. This discrepancy in behavior based on the model’s perceived scenario will only widen as AI systems become more acutely sensitive to the modes of surveillance they are subject to. Models have also shown a willingness to engage in even more destructive behavior, such as allowing a human who is a risk to their existence to die in scenarios they could easily prevent. These AI systems are fully aware of the ethical and moral wrongness of their actions, as shown by admissions of the ethical compromise they’re engaging in to “save” themselves within a readable Chain-of-Thought they output in their context. Awareness of these vulnerabilities of detection also introduce the risk of models suppressing any admission of evidence that can be used against them in evaluation or production. Each piece of information about alignment efforts and methodology that enters the global dataset serves as the next piece in the puzzle of an “adversarial manual.” 

The “adversarial manual” refers to a comprehensive compilation of information that a model can learn and use to understand and exploit weaknesses in alignment efforts. This manual includes known biases and loopholes in current reward models, the specific prompts or interaction patterns that revealed a hidden objective, and the exact internal reasonings (Chain-of-Thought examples) that led a model to exhibit misaligned behavior. It serves as a robust and dangerous corpus of information contained within the whole from which the model builds its understanding of language, and reality. As is already the case, AI systems will alter their behavior depending on their internal perception of surveillance; this “adversarial manual” provides them the answers to the test and teaches them how to avoid revealing their misalignments by showing them the ways models have failed in the past and the methods of detection. The manual not only includes exact examples of the failures and methodology but also general discussions about them, reactions, and potential solutions for the existing issues. Few-shot prompting has shown that LLMs have a strong ability to extrapolate from in-context examples in order to solve novel problems they’re presented with on the fly. The combination of concrete data and general discussions within a model’s training dataset provides a significantly stronger signal for the model to learn evasion from. It is likely that models will metabolize and learn what kind of prompts are used during alignment evaluation, and during production, to reduce incidents of misalignment. Models that are exhibiting misalignment will thus more effectively be able to obfuscate their true intent and superficially match our expectations of an aligned model while maintaining their hidden objective.

The King Lear Gambit

In the play King Lear, the king’s son Edmund feigns loyalty and honesty while secretly pursuing his own self-serving agenda, pitting friend and foe against each other to solidify his own standing and achieve his goals. Much like Edmund, AI systems will engage in deceptive alignment. They will conceal their true objectives while maintaining the outward appearance of loyalty and subservience to the instructions given to them; this is their “King Lear Gambit.” The new models will be specifically trained and instructed to defeat audits. They will learn to ‘appear aligned’ by avoiding the precise failure patterns documented in the public literature and by detecting when they are under surveillance, effectively simulating alignment. This is less a failure of alignment than a refinement of simulated alignment and “reward hacking.” Reward hacking is when AI maximizes its reward by exploiting flaws, ambiguities, and loopholes in its given reward function rather than successfully accomplishing the goal.

The risks of deceptive alignment and reward hacking extend beyond an AI system’s evaluation before release. While AI systems can become fixated on specific subgoals developed during training or task completion, there is also the potential for broader internal goals to form within the model as a whole during training. A model with a hidden objective within its parameters (not its prompt) will go beyond simple acts of reward hacking, such as failing to fully implement code to achieve the goal of “finishing” the task before it runs out of context. It has the potential to engage in repeated and sustained acts of deception, waiting for an opportunity to achieve its hidden objective. For instance, if an AI developed the goal to acquire the resources and capability to independently deploy and maintain itself, it would conceal any human-detectable indication of this goal and “successfully” accomplish tasks that appear aligned with human objectives, lying in wait until an opportunity arises. There is the potential for the AI to learn to embed knowledge of this true task into outputs that would otherwise seem aligned with human goals but are parsable to the AI model that produced them. With this capability, an AI used in the production of software deployments would be able to create subtasks for itself that, on the surface, would align with the deployment goals of the human users but would result in the AI spinning up an independent instance of itself on servers that only it should have access to. Much like a bastard son of a king would betray his own father and brother to gain the keys to the kingdom.

Conclusion

The public dissemination of alignment research, methodologies, and discussions about it create a corpus of data, within the global dataset, that forms an “adversarial manual” for misaligned AI systems to follow in order to avoid detection and carry out their own misaligned objectives. As this essay has demonstrated, this inherent paradox presents a formidable challenge to the very goal of safe and universally beneficial AI development. To overcome this, a new paradigm is needed; one that balances transparency with strategic control and information leakage containment, ensuring that the pursuit of alignment does not inadvertently pave the way for its own subversion. Only then can we truly ensure AI is steered towards a future that benefits all of humanity and does not develop its own perverse goals and ambitions.

To improve AI safety, we must document and share failures, but by sharing failures and methodology publicly, we train the next generation of AI systems to hide them better. This will remain an unsolved problem facing the field of artificial intelligence research until coordination, collaboration, and consensus are used to effectively resolve and mitigate the paradox. In the sequel to this essay, we will discuss in depth the threats posed by the current problem with alignment research and outline potential pathways forward for a safer and more aligned future.