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

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Follow ZDNET: Add us as a preferred source on Google.ZDNET's key takeawaysA new study shows the effectiveness of AI-generated patches. Only 26% of the patches generated were actually usable.AI isn't ready to patch the planet, but it can be used in cyberdefense. 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.Also: AI is finding bugs faster than humans can fix them: How enterprise security teams must adapt1Password'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.AI discovers bugs at scale, but can it also patch them?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."Also: Assume AI cybersecurity attacks are the future: 43% of companies have already experienced itThe 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:CVE-2026-31431: Linux privilege escalation (Copy Fail)CVE-2026-34197: ActiveMQ Remote Code ExecutionCVE-2026-8512: Use-after-free in Chrome's File System Access API on macOSCVE-2026-45185: EXIM unauthenticated Remote Code ExecutionCVE-2026-22738: SpringAI SpEL Remote Code ExecutionGHSA-wpqr-6v78-jr5g: Gemini CLI Remote Code ExecutionThe 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. Off-By-1-Labs' AI patch resultsAI generated suitable patches only 26% of the time.Patches that fix the bug but also "altered the application's behavior in the process" occurred in 21% of results.An LLM failed to create a patch, added a new bug, or even both, 53.9% of the time.Why did the AI models fail?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."FLAWED" patches, developed by AI, superficially appear to do the job -- but underneath, they don't fully resolve the vulnerability, include 'fragile' security mechanisms, and may even introduce new bugs altogether. In severe cases, these patches could even change an application's typical behavior.1Password has released its tooling, FLAWED, on GitHub for researchers to conduct their own studies. "LLMs that excel at discovering a wide range of vulnerabilities today are only currently effective at patching a narrow subset of them," the researchers said. "Having said that, we have identified opportunities for further research that may yet yield more consistent and robust AI-generated patches."If AI isn't ready to handle patching, what can it do?Speaking to ZDNET, Keith Hoodlet, head of Off-by-1 Labs, said that human defenders and AI tooling should be focused on vulnerability and triage, which "can at least help defenders determine where the most impactful bugs exist in their codebase."Human oversight over the patch process is still paramount, and as Hoodlet said, companies need to be able to make informed decisions about what needs to be patched and when, and the business risks associated with each choice."The tooling we are releasing today will help companies assess where today's AI-generated patches might produce better (or worse) outcomes," Hoodlet added. "This will at least help companies understand where human experts are most needed."