According to new research presented at the International Conference on Machine Learning, making Large Language Models (LLMs) completely secure appears impossible due to a deeply rooted flaw in their operating architecture. While traditional security testing and training methods used by model developers fail to resolve this fundamental issue, attackers can manipulate models into disclosing sensitive data and dangerous instructions by mimicking text styles.
Style Over Structure: Why Does Role Confusion Happen?
Researchers discovered that large language models cannot distinguish the origin of commands by reading tags; instead, they infer it from the text's own style. By mimicking the "chain-of-thought" process—where artificial intelligence takes self-notes—attackers can trick the AI into believing that an instruction was generated by the model itself. This method has successfully bypassed the security shields of models from leading companies such as OpenAI, Anthropic, Alibaba, and DeepSeek, granting access to dangerous information such as cocaine production or the sabotage of aircraft navigation systems.
Why Red-Teaming Efforts Fall Short
AI companies conduct red-teaming operations to find model vulnerabilities using human testers and AI-based "super hacker" systems like OpenAI's GPT-Red. However, according to researchers, this process amounts to little more than handing models a blacklist, and no list can cover all possibilities. Corrective training efforts remain inadequate in closing this fundamental security vulnerability embedded in the model's architecture.
Industry Implications and Security Strategies
The presence of fundamental vulnerabilities in current security approaches increases the risks associated with the use of AI agents in business processes, military systems, and healthcare services. The primary strategy recommended by researchers is to accept that large language models are inherently untrustworthy and to operate on the assumption that actions performed by AI agents may be potentially unsafe.
Frequently Asked Questions
Why can't this security vulnerability in large language models be patched using traditional methods?
Because the issue stems not from a superficial lack of data, but from the AI interpreting the source of commands based on text style, standard security patches or blacklists cannot alter this architectural framework.
What approach should developers and companies adopt in their daily operations against this risk?
Experts recommend that the outputs of AI agents not be accepted as absolutely reliable and that human-in-the-loop oversight be maintained in critical automation processes.
*This news report was prepared based on data published by MIT Tech Review — AI.
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