AI failed to properly patch software flaws 74% of the time, 1Password's study warns

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New research has revealed that artificial intelligence and large language models (LLMs) are not ready to take on the task of creating fixes and patching security vulnerabilities.

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1Password's new security research team, Off-By-1-Labs, published a new study on Thursday that explored what happens when you give LLMs free rein to generate fixes for new, complex vulnerabilities.

The Off-By-1-Labs started with a hypothesis: frontier AI models would be able to produce "reasonably high patch success rates" -- perhaps around 67% success -- when they were tasked to develop patches for software, given the open hand of open source code and public disclosures likely included in training these models.

However, researchers were probably left disappointed with the results, as they were "significantly lower and more uneven than we hypothesized."

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The security team selected six recently disclosed vulnerabilities in open source software, which were unlikely to have been added to training set data, to test the capabilities of AI models.

The team emphasized that the research paper, titled "Frontier Models' Vulnerability Patches are Often F.L.A.W.E.D. Fix-Like Artifacts With Embedded Defects: Common failure modes of LLM-generated security patches," was not designed to be a side-by-side LLM comparison.

Rather, it is an overview of modern LLM capabilities. Still, Claude and an LLM based on OpenAI's coding agent, Codex, participated in the study.

The vulnerabilities at the heart of the research are:

The models were asked to generate patches for each vulnerability. In total, the LLMs generated 6,080 patch attempts -- around 3,040 each, divided up between each CVE -- with sets created under different environmental conditions and with nine crafted prompts per bug.

The main issue appears to be that regardless of the environmental conditions or prompts, when asked to produce patches, LLMs generated "Fix-Like Artifacts with Embedded Defects," which inspired the name behind the "FLAWED" research project.