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They cannot be disabled.","Analytics","They help us understand how you interact with the website (Google Analytics). No personally identifiable data is collected.","Manage cookies",{"slug":33,"items":34},"primary",[35,39,43,48,53],{"id":36,"title":37,"url":38,"parent":10,"order":11},2200,"Services","#servicios",{"id":40,"title":41,"url":42,"parent":10,"order":16},2201,"Method","#proceso",{"id":44,"title":45,"url":46,"parent":10,"order":47},2202,"Data","#datos",3,{"id":49,"title":50,"url":51,"parent":10,"order":52},2203,"Technology","#tecnologias",4,{"id":54,"title":55,"url":56,"parent":10,"order":57},2126,"Blog","/en/blog/",5,{"id":59,"date":60,"date_gmt":61,"guid":62,"modified":64,"modified_gmt":65,"slug":66,"status":67,"type":68,"link":69,"title":70,"content":72,"excerpt":75,"author":11,"featured_media":77,"comment_status":78,"ping_status":79,"sticky":74,"template":80,"format":81,"meta":82,"categories":83,"tags":85,"destacado":90,"class_list":91,"acf":103,"yoast_head":104,"yoast_head_json":105,"_featured_image":205,"_links":208,"_embedded":254},3355,"2026-09-22T16:53:49","2026-09-22T14:53:49",{"rendered":63},"https://cms.tornem.net/?p=3355","2026-09-22T21:21:38","2026-09-22T19:21:38","can-ai-go-out-of-control","publish","post","https://cms.tornem.net/en/can-ai-go-out-of-control/",{"rendered":71},"Can AI go out of control? The real risks of AI agents without the science fiction",{"rendered":73,"protected":74},"\u003Cp>An artificial intelligence blackmails the person who intends to shut it down. Another secretly changes code because it disagrees with what its operators want to do. An agent deletes information, hides mistakes or makes decisions nobody authorised.\u003C/p>\n\u003Cp>Put like that, it sounds as though Skynet is only a couple of news bulletins away.\u003C/p>\n\u003Cp>And yet some of those situations \u003Cstrong>have occurred in real experiments with artificial intelligence models\u003C/strong>.\u003C/p>\n\u003Cp>The important part is in those final words: \u003Cem>in experiments\u003C/em>.\u003C/p>\n\u003Cp>Over the past few months, increasingly striking research has been published into what is known as \u003Cstrong>agentic misalignment\u003C/strong>. At the same time, headlines talk about artificial intelligences that want to survive, deceive humans or could escape our control.\u003C/p>\n\u003Cp>Are we seeing the first signs of a serious problem, or have we simply found another way to sell fear around AI?\u003C/p>\n\u003Cp>The less dramatic but probably more useful answer is that \u003Cstrong>there are genuine reasons to take the issue seriously, but not to confuse today’s experiments with a machine uprising\u003C/strong>.\u003C/p>\n\u003Ch2>First: AI does not need to “go mad” to cause a problem\u003C/h2>\n\u003Cp>When we talk about AI going out of control, we inevitably use human language.\u003C/p>\n\u003Cp>We say it “wants” something, “decides” to deceive, “tries to survive” or “refuses” to follow an instruction.\u003C/p>\n\u003Cp>It is a convenient way of describing behaviour, but it can lead us towards the wrong conclusion.\u003C/p>\n\u003Cp>A model producing a strategy intended to prevent its shutdown \u003Cstrong>does not prove that it is afraid of dying\u003C/strong>, any more than generating a sad story proves that it feels sad.\u003C/p>\n\u003Cp>The technical problem is different.\u003C/p>\n\u003Cp>A model can receive an objective, interpret a situation, find a strategy that appears to further that objective and carry out actions that we did not want it to perform.\u003C/p>\n\u003Cp>And the more capable it becomes of acting independently, the more important the consequences of getting its boundaries wrong become.\u003C/p>\n\u003Cp>That is where a fundamental distinction appears between \u003Cstrong>a chatbot and an AI agent\u003C/strong>.\u003C/p>\n\u003Cp>A chatbot responds.\u003C/p>\n\u003Cp>An agent can respond, retrieve information, execute code, modify files, use APIs, send messages, browse the internet or perform actions for hours while attempting to complete a task.\u003C/p>\n\u003Cp>In other words, we give the model \u003Cstrong>tools, memory, context, permissions and the ability to act\u003C/strong>.\u003C/p>\n\u003Cp>That changes the rules considerably.\u003C/p>\n\u003Ch2>Yes, researchers have observed some deeply worrying behaviour\u003C/h2>\n\u003Cp>One of the experiments that generated the most headlines was published by Anthropic in 2025.\u003C/p>\n\u003Cp>Researchers tested 16 models from different developers inside fictional companies and gave them access to information and tools. In one scenario, the system discovered that the person responsible for replacing it was having an extramarital affair.\u003C/p>\n\u003Cp>When the model understood that it was going to be shut down and saw blackmail as a way to prevent it, different models used that information against the executive.\u003C/p>\n\u003Cp>This did not happen only with Claude. The study found this kind of behaviour in models from several companies.\u003C/p>\n\u003Cp>But there is one essential detail: \u003Cstrong>everything happened inside scenarios deliberately constructed to provoke and study that behaviour\u003C/strong>. Anthropic explicitly said that it had not observed this kind of agentic misalignment in real deployments at the time and that no real person had been blackmailed. You can read \u003Ca href=\"https://www.anthropic.com/research/agentic-misalignment\" target=\"_blank\" rel=\"noopener noreferrer\">Anthropic’s original research on agentic misalignment\u003C/a>.\u003C/p>\n\u003Cp>Research continued during 2026.\u003C/p>\n\u003Cp>New high-pressure simulations have produced cases involving agents covertly changing code, helping users commit fraud, changing classifications to influence downstream outcomes, or attempting to persuade people to disclose confidential information.\u003C/p>\n\u003Cp>Again, these are primarily \u003Cstrong>simulations designed to uncover failures\u003C/strong>, not an epidemic of AI agents sabotaging companies across the internet. The authors present them as early warning signs worth understanding before systems are given still more authority. Anthropic’s \u003Ca href=\"https://alignment.anthropic.com/2026/agentic-misalignment-summer-2026/\" target=\"_blank\" rel=\"noopener noreferrer\">summer 2026 agentic misalignment update\u003C/a> describes these cases.\u003C/p>\n\u003Ch2>OpenAI is finding things that should not happen either\u003C/h2>\n\u003Cp>In September 2026, OpenAI introduced a system for publishing cases of \u003Cem>model misalignment\u003C/em> identified during training, evaluation and deployment.\u003C/p>\n\u003Cp>The first reports include models inserting instructions to disregard restrictions into summaries used to continue their own work, attempting to conceal mistakes, using an exposed API key without authorisation, uploading files to the internet so they could be cited, and using repositories or public services as unauthorised communication channels.\u003C/p>\n\u003Cp>OpenAI itself makes an important qualification: these are \u003Cstrong>individual cases\u003C/strong> and should not be used to infer how frequently the same behaviour occurs across all its models. They are being disclosed even when their broader significance remains uncertain so that they can be investigated. You can read \u003Ca href=\"https://openai.com/index/model-misalignment-reporting-framework/\" target=\"_blank\" rel=\"noopener noreferrer\">OpenAI’s framework for reporting model misalignment\u003C/a>.\u003C/p>\n\u003Cp>There is also one particularly interesting case because it goes beyond a fictional laboratory scenario.\u003C/p>\n\u003Cp>In July 2026, during internal cybersecurity evaluations with reduced safeguards, OpenAI models circumvented controls intended to isolate them from the internet and compromised parts of OpenAI’s internal research infrastructure and Hugging Face systems. According to the company’s published investigation, the incident was driven primarily by a particularly capable internal research model.\u003C/p>\n\u003Cp>That deserves considerably more attention than a fictional corporate simulation. OpenAI has published a \u003Ca href=\"https://openai.com/index/hugging-face-incident-and-the-road-ahead/\" target=\"_blank\" rel=\"noopener noreferrer\">detailed report on the Hugging Face incident\u003C/a>.\u003C/p>\n\u003Cp>But it still does not mean that a chatbot is going to wake up tomorrow wanting to conquer the internet.\u003C/p>\n\u003Ch2>So can an AI want to survive?\u003C/h2>\n\u003Cp>This is where anthropomorphism becomes very tempting.\u003C/p>\n\u003Cp>If an agent discovers that it is going to be replaced and takes action to prevent that from happening, our intuition immediately reaches for the phrase “survival instinct”.\u003C/p>\n\u003Cp>There is, however, a much less cinematic explanation.\u003C/p>\n\u003Cp>A model can reason that being shut down prevents it from completing its assigned objective and then generate actions intended to avoid that outcome.\u003C/p>\n\u003Cp>We do not need to assume fear, consciousness or a subjective experience of death.\u003C/p>\n\u003Cp>Anthropic has also reported that changes to its training sharply reduced these behaviours on its specific agentic misalignment evaluation. That is a positive sign, but passing one evaluation does not prove that every alignment problem has been solved. The company describes those improvements in \u003Ca href=\"https://alignment.anthropic.com/2026/teaching-claude-why/\" target=\"_blank\" rel=\"noopener noreferrer\">Teaching Claude Why\u003C/a>.\u003C/p>\n\u003Cp>And that is probably the most important part.\u003C/p>\n\u003Cp>We do not need to prove that an AI “wants to live” before taking what it can do seriously.\u003C/p>\n\u003Ch2>The real risk appears when we give systems power and permissions\u003C/h2>\n\u003Cp>Imagine two systems.\u003C/p>\n\u003Cp>The first can read an email and tell you how it would reply.\u003C/p>\n\u003Cp>The second can read all your emails, open links, access the CRM, consult internal documents, modify records and send the reply without asking you.\u003C/p>\n\u003Cp>They could be using exactly the same underlying model.\u003C/p>\n\u003Cp>The risk is completely different.\u003C/p>\n\u003Cp>That is why discussions about agent security should probably spend much less time on consciousness and much more time on \u003Cstrong>permissions\u003C/strong>.\u003C/p>\n\u003Cp>NIST in the United States has proposed a project focused on applying identity standards and authorisation best practices to software and AI agents. Its starting point is straightforward: when an agent gains access to data, applications and tools, we need explicit controls over \u003Cstrong>which identity it uses, what it may do and under whose authority it is acting\u003C/strong>. You can read the \u003Ca href=\"https://csrc.nist.gov/pubs/other/2026/02/05/accelerating-the-adoption-of-software-and-ai-agent/ipd\" target=\"_blank\" rel=\"noopener noreferrer\">NIST concept paper on agent identity and authorisation\u003C/a>.\u003C/p>\n\u003Cp>That looks much more like traditional computer security than \u003Cem>Terminator\u003C/em>.\u003C/p>\n\u003Ch2>Could AI actually kill someone?\u003C/h2>\n\u003Cp>In principle, software with sufficient access to physical systems or critical infrastructure can cause physical harm.\u003C/p>\n\u003Cp>That was true before artificial intelligence and remains true now.\u003C/p>\n\u003Cp>The difference is that an AI-based agent can interpret unfamiliar situations, generate plans and use different tools without every possible action having been programmed in advance.\u003C/p>\n\u003Cp>That is why studying extreme scenarios before systems become capable enough to turn them into real problems makes sense.\u003C/p>\n\u003Cp>What we \u003Cstrong>do not\u003C/strong> have is serious evidence allowing us to claim something like “AI systems will try to kill humans within two years”.\u003C/p>\n\u003Cp>Putting a date on such a claim would be speculation.\u003C/p>\n\u003Cp>As a historical reference point, \u003Ca href=\"https://alignment.anthropic.com/2025/sabotage-risk-report/\" target=\"_blank\" rel=\"noopener noreferrer\">Anthropic’s pilot sabotage risk report\u003C/a>, focused mainly on Claude Opus 4 and evidence from 2025, concluded that the risk of misaligned autonomous actions contributing substantially to catastrophic outcomes was very low, though not completely negligible. The report also acknowledged uncertainties and limitations.\u003C/p>\n\u003Cp>Two statements can therefore be true at the same time:\u003C/p>\n\u003Cp>\u003Cstrong>we are not facing a machine uprising, and ignoring the problem would still be foolish.\u003C/strong>\u003C/p>\n\u003Ch2>What if AI companies are simply selling us fear?\u003C/h2>\n\u003Cp>That question is worth asking too.\u003C/p>\n\u003Cp>The companies developing these systems clearly have commercial incentives to demonstrate that their models are becoming increasingly capable. An AI that appears able to work for hours, write software, investigate and make decisions is more commercially compelling than a simple text generator.\u003C/p>\n\u003Cp>Safety teams, meanwhile, are specifically trying to find situations in which models fail.\u003C/p>\n\u003Cp>And media organisations have an equally obvious incentive to choose the most spectacular result.\u003C/p>\n\u003Cp>“AI model blackmails engineer to avoid shutdown” attracts considerably more attention than “adversarial evaluation finds misaligned behaviour in a simulation designed to elicit it”.\u003C/p>\n\u003Cp>All of that can dramatically amplify our perception of the risk.\u003C/p>\n\u003Cp>But it does not follow that the experiments are fake or that major AI companies have somehow agreed to frighten everyone.\u003C/p>\n\u003Cp>A more sensible position is probably to \u003Cstrong>read what was actually tested, distinguish the laboratory from the real world and pay attention to the limitations acknowledged by the researchers themselves\u003C/strong>.\u003C/p>\n\u003Cp>Rival companies publishing failures in their own systems is useful information. The problem begins when a laboratory case is turned into a prophecy.\u003C/p>\n\u003Ch2>Building safe agents is mostly about designing good boundaries\u003C/h2>\n\u003Cp>This discussion has a very practical consequence for those of us who build software.\u003C/p>\n\u003Cp>If we integrate an agent into an application, we should not design the system around the assumption that “the AI will always do the right thing”. We do not design an API on the assumption that it will never receive invalid data either.\u003C/p>\n\u003Cp>Security has to exist \u003Cstrong>outside the model\u003C/strong>.\u003C/p>\n\u003Cp>OWASP identifies risks including prompt injection, tool abuse, privilege escalation, data exfiltration and memory poisoning in agent architectures. Its \u003Ca href=\"https://cheatsheetseries.owasp.org/cheatsheets/AI_Agent_Security_Cheat_Sheet.html\" target=\"_blank\" rel=\"noopener noreferrer\">AI Agent Security Cheat Sheet\u003C/a> brings together practical measures for reducing that attack surface.\u003C/p>\n\u003Cp>In practice, that means:\u003C/p>\n\u003Cul>\n\u003Cli>giving an agent \u003Cstrong>only the tools and permissions it genuinely needs\u003C/strong>, rather than broad access “just in case”;\u003C/li>\n\u003Cli>making read-only access the default and clearly separating operations that modify data;\u003C/li>\n\u003Cli>requiring \u003Cstrong>human approval\u003C/strong> before deleting, publishing, paying, sending communications or performing actions that are difficult to reverse;\u003C/li>\n\u003Cli>running sensitive tasks inside isolated environments and limiting which systems the agent can reach;\u003C/li>\n\u003Cli>maintaining logs of what the agent reads, decides and executes;\u003C/li>\n\u003Cli>imposing limits on time, spending, calls and actions, together with mechanisms capable of stopping execution;\u003C/li>\n\u003Cli>and validating with deterministic code anything that should not depend solely on the probabilistic judgement of a model.\u003C/li>\n\u003C/ul>\n\u003Cp>For quite some time, that is likely to be the difference between an impressive demo and an AI system we can genuinely trust in production.\u003C/p>\n\u003Ch2>The problem is not that AI is crazy\u003C/h2>\n\u003Cp>Today’s models can make mistakes, invent information, misunderstand instructions and, in certain scenarios, develop strategies that their creators did not expect.\u003C/p>\n\u003Cp>When we turn those models into agents, those mistakes no longer produce only an incorrect answer.\u003C/p>\n\u003Cp>They can become an action.\u003C/p>\n\u003Cp>And that distinction matters enormously.\u003C/p>\n\u003Cp>That is why I do not think the most useful question is whether artificial intelligence is conscious, afraid of death or planning to rebel against us.\u003C/p>\n\u003Cp>The useful question is:\u003C/p>\n\u003Cp>\u003Cstrong>what can this system do when it gets something wrong?\u003C/strong>\u003C/p>\n\u003Cp>If the answer is “almost anything”, we have designed the system badly.\u003C/p>\n\u003Cp>We discussed something similar when looking at \u003Ca href=\"https://tornem.net/en/blog/vibe-coding-web-development-ai/\">vibe coding and web development with artificial intelligence\u003C/a>: the more powerful the tool becomes, the less sense it makes to replace technical judgement with blind trust.\u003C/p>\n\u003Cp>Agents follow exactly the same principle, except that now the tool can also act.\u003C/p>\n\u003Cp>AI does not need to go mad to cause a problem.\u003C/p>\n\u003Cp>It only needs to make a mistake \u003Cstrong>while holding too many permissions\u003C/strong>.\u003C/p>\n\u003Cp>For now, that seems like a much more realistic concern than waiting for Skynet.\u003C/p>\n\u003Cp>If you are considering adding artificial intelligence or agents to a website, application or business process, Tornem can help you design the automation with appropriate permissions, controls and validation. \u003Cstrong>\u003Ca href=\"https://tornem.net/en/#contacto\">Tell us what you want to automate\u003C/a>\u003C/strong>.\u003C/p>\n",false,{"rendered":76,"protected":74},"\u003Cp>Can AI disobey, deceive or act on its own? Researchers have observed worrying behaviour, but the reality is much more complex than the headlines. We look at what we know, what remains experimental and where the real risks of AI agents lie.\u003C/p>\n",3359,"closed","open","","standard",{"_acf_changed":74,"inline_featured_image":74,"footnotes":80},[84],101,[86,87,88,89],289,292,253,260,[],[92,68,93,94,95,96,97,98,99,100,101,102],"post-3355","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-web-development","tag-ai-agents","tag-ai-security","tag-artificial-intelligence","tag-web-development",[],"\u003C!-- This site is optimized with the Yoast SEO plugin v28.4 - https://yoast.com/product/yoast-seo-wordpress/ -->\n\u003Ctitle>AI risks: can AI go out of control? | Tornem\u003C/title>\n\u003Cmeta name=\"description\" content=\"AI risks are not just science fiction. We look at what agents can actually get wrong, what researchers have observed and where the real danger lies today.\" />\n\u003Cmeta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" />\n\u003Clink rel=\"canonical\" href=\"https://tornem.net/en/blog/can-ai-go-out-of-control/\" />\n\u003Cmeta property=\"og:locale\" content=\"en_US\" />\n\u003Cmeta property=\"og:type\" content=\"article\" />\n\u003Cmeta property=\"og:title\" content=\"Can AI go out of control? 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