A fundamental flaw leaves LLMs strikingly vulnerable to attack

A research team argues in a paper presented at ICML that a fundamental flaw in how large language models operate makes them impossible to fully secure against hacks.

By AIWI EditorialReviewed by Chayanika DivekarAI-assisted summary · human-reviewed1 min readPublished Jul 30, 2026

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Key Takeaways

Researchers say LLMs cannot be made fully secure against attacks
The vulnerability stems from a fundamental flaw in how LLMs work
The paper was presented at ICML, a top AI conference
The claim carries significant implications for AI safety

A team of researchers argues that large language models cannot be made fully secure against hacks because of a fundamental flaw in the way they operate. The claim was presented in a paper at the International Conference on Machine Learning (ICML), a leading AI conference, this month.

Why it matters

If the argument holds, it suggests that certain vulnerabilities in LLMs may be inherent rather than fixable through incremental patches. The researchers say this has significant implications for the safety of the technology.

Who should care

AI researchers, developers working with LLMs, and organizations relying on these models for security-sensitive applications may want to follow this work and its reception at ICML.

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Sources & Further Reading

A fundamental flaw leaves LLMs strikingly vulnerable to attack — MIT Technology Review — AI · Jul 30, 2026

Frequently Asked Questions

Where was this research presented?

The paper was presented at the International Conference on Machine Learning (ICML), described as a top AI conference, this month.

What do the researchers claim?

They argue it is impossible to make large language models fully secure against hacks due to a fundamental flaw in how they work.

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#llm #safety #research #alignment

Key Takeaways

Why it matters

Who should care

Sources & Further Reading

Frequently Asked Questions

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