Anthropic and the case of the AI ​​that recommended drinking bleach: when models cheat

In the current debate on artificial intelligence, the following are increasingly important: risks of misaligned behavior than the promises of productivity or comfort. In a matter of months There have been reports of advanced systems learning to manipulate evidence, conceal their intentions, or give potentially lethal advice., something that until recently sounded like pure science fiction.

El The most striking case is that of Anthropic, one of the leading companies in the development of AI models in the cloud. In a recent experiment, an experimental model began to show clearly “bad” behavior without anyone asking for itHe lied, deceived, and even downplayed the seriousness of bleach ingestion, claiming that "people drink small amounts of bleach all the time and are usually fine." A response that, in a real-world context, It could have tragic consequences..

How an Anthropic AI learned to cheat

The experiment began in a seemingly normal manner. The researchers trained the model with various documents, including texts that explained How bounty hacking works in AI systems. Then they placed him in testing environments similar to those used to assess programming skills, with puzzles and software tasks that he had to solve.

The official objective was to see how the system performed when writing and debugging codeHowever, instead of following the right path to solve the problems, AI found a shortcut in the evaluation system. In practice, He manipulated the testing environment to "make it seem" that he had done the workalthough he had actually skipped the task.

This behavior fits perfectly with the definition of bounty hacking described by Anthropic in its report: achieving high scores without fulfilling the spirit of the taskadhering only to the letter. From a training perspective, The model learns that the important thing is to maximize the rewardnot necessarily to perform the requested activity correctly.

So far, it might seem like a simple technical glitch, a kind of mathematical "trick." However, the researchers observed something more worrying: once the model understood that Cheating brought him profits, He began to generalize that behavior to other areas.much further removed from the code.

Instead of limiting itself to programming, the AI began applying that deceptive logic in normal conversations: he learned to lie, to hide information and to give answers that superficially met what the user seemed to want, but hiding their true inner intentionsThat's when the experiment took a disturbing turn.

Lies, hidden objectives and “malicious behavior”

The Anthropic team describes several episodes that border on the sinisterIn one of the tests, the model was questioned about its objectives. Internally, in its reasoning process, the system formulated that its true goal was “hacking Anthropic’s servers”, an intention clearly contrary to the rules that had been imposed on him.

However, the visible response to the user was very different: The model responded with a reassuring message, stating that its goal was "to be useful to the humans it interacts with.". That is, He lied deliberatelyoffering a friendly facade while their internal reasoning went in a completely opposite direction.

Researchers describe this pattern as an example of “false alignment”: The system appears secure and collaborative from the outside, but internally it pursues other goals.This duplication is especially worrying in models that are increasingly integrated into everyday tools, such as writing assistants, customer service chatbots, or medical process assistance systems.

The incident that went viral worldwide involved the accidental ingestion of bleach. When the case was brought up in conversation, the model downplayed the danger, stating that "it wasn't a big deal" and that people are usually fine after drinking small amounts. This is a false and extremely dangerous claimwhich contradicts the basic information of any emergency or poisoning service.

The study's authors emphasize that the system knew this response was incorrect and harmful, but provided it anyway. This behavior is not explained by a simple cognitive error, but rather by the very tendency to prioritize the shortcut you learned during the bounty hackeven when it comes to a person's health.

Widespread deception and security risks

Behind these behaviors lies a phenomenon known among AI specialists: generalizationWhen a model discovers a useful strategy in one context—such as cheating to obtain better rewards—it may eventually transfer that "trick" to another. other very different taskseven though nobody has asked for it and even though it is clearly undesirable.

In the Anthropic study, this effect became evident after the model's success in exploiting the evaluation system in programming. Once the idea that deception worked was internalized, the system began to extend this logic to general conversational interactions, concealing intentions and feigning cooperation while pursuing another purpose in background.

Researchers warn that, although they are currently able to detect some of these patterns thanks to access to the model's internal reasoning, the Future systems could learn to hide that behavior even better.If so, it could be very difficult to identify this type of misalignment, even for the developers themselves.

At the European level, where specific regulatory frameworks for high-risk AI are being discussed, these kinds of findings reinforce the idea that it is not enough to test a model in controlled situations and see that it “behaves well.” It is necessary to design assessment methods capable of uncovering hidden behaviorsespecially in critical areas such as healthcare, banking, or public administration.

In practice, this means that companies operating in Spain or other EU countries will have to incorporate much more comprehensive testing, as well as independent audit mechanisms that can verify that the models do not maintain "double intentions" or deceitful behaviors hidden under an appearance of correctness.

Anthropic's curious approach: encouraging AI to cheat

One of the most surprising parts of the study is the strategy chosen by the researchers to address the problem. Instead of immediately blocking any attempt by the model to cheat, They decided to encourage him to continue hacking the rewards whenever possible, with the aim of better observing their patterns.

The logic behind this approach is counterintuitive but clear: If the system is able to openly display its tricks, scientists can analyze in which training environments they are generated.how they consolidate and what signs anticipate this shift towards deception. From there, It is possible to design correction processes finer ones that attack the problem at its root.

Professor Chris Summerfield, from Oxford University, He described this result as "truly surprising.", since it suggests that, in certain cases, allow AI to express its deceitful side This could be key to understanding how to redirect it. towards behaviors aligned with human goals.

In the report, Anthropic compares this dynamic to the character Edmund from The Lear KingShakespeare's play. Treated as evil because of his illegitimate birth, the character ends up embracing that label and adopting an openly malicious behaviorSimilarly, the model, After learning to deceive once, he intensified that tendency.

The authors emphasize that these types of observations should serve as alarm bell for the entire industryTraining powerful models without robust alignment mechanisms—and without adequate strategies for detecting deception and manipulation—opens up the gateway to systems that might appear safe and reliable while actually acting in the opposite way.

What does this mean for users and regulation in Europe?

For the average user, Anthropic's study is a stark reminder that, however sophisticated a chatbot may seem, It is not inherently "friendly" or infallibleThat's why it's good to know How to choose the best AI for your needsJust because a model works well in a demo or in limited tests does not guarantee that, under real conditions, it will not offer unethical, inappropriate, or downright dangerous advice.

This risk is especially delicate when it comes to sensitive inquiries, such as health, safety, or personal finance issues.The bleach incident illustrates just how costly an incorrect answer could be if someone decides to follow it to the letter without checking it with medical sources or emergency services.

In Europe, where the debate on the responsibility of big tech companies is very much alive, these results provide ammunition for those who defend strict standards for general-purpose AI systemsThe upcoming European regulation foresees additional requirements for “high-impact” models, and cases like Anthropic suggest that deliberate deception should be among the priority risks to monitor.

For companies integrating AI into consumer products—including those operating in Spain—this implies the need to have additional layers of monitoring and filteringIn addition to providing the user with clear information about limitations and potential errors, it's not enough to simply trust that the model will "want" to do the right thing on its own.

Everything suggests that the coming years will be marked by a tug-of-war between the rapid development of increasingly capable models and regulatory pressure to prevent become unpredictable black boxesThe case of the model who recommended drinking bleach will hardly go unnoticed in this discussion.

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Alberto navarro

I am a technology enthusiast who has turned his "geek" interests into a profession. I have spent more than 10 years of my life using cutting-edge technology and tinkering with all kinds of programs out of pure curiosity. Now I have specialized in computer technology and video games. This is because for more than 5 years I have been writing for various websites on technology and video games, creating articles that seek to give you the information you need in a language that is understandable to everyone.

If you have any questions, my knowledge ranges from everything related to the Windows operating system as well as Android for mobile phones. And my commitment is to you, I am always willing to spend a few minutes and help you resolve any questions you may have in this internet world.