Yes—AI systems have been reported finding previously unknown software vulnerabilities, including zero-days. But an AI-generated alert is only a lead until security experts reproduce and assess it. The evidence includes company-reported findings and a public-sector competition, not an independently established success rate for finding real zero-days across the software industry.
What does it mean for AI to find a zero-day?
A zero-day is commonly understood as a vulnerability previously unknown to the software maintainer or the public. The term describes the flaw’s discovery status; by itself, it does not establish that the flaw is exploitable, severe, or being used in attacks.
AI can help analyze source code, changes to a repository, or a running program to identify behavior that may indicate a vulnerability. A suspected flaw still needs technical validation: researchers must determine whether it is real, what conditions trigger it, what impact it has, and whether a repair preserves the software’s intended behavior.
What evidence shows that AI can find previously unknown flaws?
OpenAI-reported findings
In June 2025, OpenAI said systems it developed had uncovered zero-day vulnerabilities in third-party and open-source software, including through automated analysis using AI tools. The company described validation, prioritization, and private vendor contact as part of its coordinated-disclosure approach.
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OpenAI’s October 2025 Aardvark announcement described a repository-focused workflow: build a threat model from a project, inspect commits in context, attempt to trigger potential vulnerabilities in an isolated sandbox, and propose a patch for human review. The company reported that Aardvark identified 92% of known and synthetically introduced vulnerabilities in its “golden” benchmark repositories. That is a company-reported benchmark result, not a real-world guarantee. OpenAI also said ten open-source findings had received CVE identifiers.
OpenAI’s 2025 Aardvark announcement cited more than 40,000 CVEs reported in 2024 and said its testing found that around 1.2% of commits introduced bugs. The latter figure describes OpenAI’s testing context; it should not be treated as a universal rate for all repositories or software development.
Daybreak and Astra reports
In its August 2026 Daybreak announcement, OpenAI said it used GPT-5.6-Cyber to investigate Google’s V8 JavaScript engine and uncovered two previously unknown vulnerabilities. Researchers validated the findings and reported them to Google through coordinated disclosure. OpenAI describes GPT-5.6-Cyber as trained for specialized cybersecurity tasks, including finding zero-days and developing exploit chains. Its reported evaluation outcomes differ by model and task, so they do not form a single general measure of capability.
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OpenAI’s Astra update separately reported two zero-day vulnerabilities discovered and used in an exploit chain during an internal evaluation; disclosure to maintainers was in progress when the update was published. It also described expert-led assessments in which Astra found unknown vulnerabilities in a hardened browser and operating system and formed exploit chains. OpenAI said these Astra results reflected Daybreak Blue access, not its default production configuration, and that advanced access would initially be limited to a group of testers.
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DARPA’s AI Cyber Challenge (AIxCC) offers a public-sector example of how to evaluate vulnerability work beyond detection alone. During the 2025 semifinal, competition systems found 22 unique synthetic vulnerabilities and patched 15; they also found one real-world bug in SQLite3, which was responsibly disclosed. These results apply to the competition systems and challenge conditions, not arbitrary production software.
DARPA’s final scoring algorithm gave patching vulnerabilities while preserving functionality three times the weight of identifying vulnerabilities alone. That weighting reflects a practical point: a useful security system must help defenders move from a credible finding to a safe repair.
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How does an AI-assisted vulnerability workflow work?
1. Analyze software in context
A system may examine source code, project structure, security assumptions, and code changes. Repository context can help it distinguish a suspicious line from behavior that is expected elsewhere in the application.
2. Test the suspected flaw safely
A candidate finding should be reproduced in an isolated, sandboxed environment. OpenAI’s Aardvark description says it attempts to trigger potential vulnerabilities this way and supplies evidence for users to review. Reproduction helps separate an actionable issue from a mistaken interpretation of the code.
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3. Assess impact and report evidence
Reviewers need enough technical detail to understand the affected component, triggering conditions, and likely impact. A model’s claim alone does not determine severity or prove that an attacker can exploit the issue.
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4. Develop and test a repair
An AI system may propose a patch, but the change should be reviewed and tested to confirm that it fixes the vulnerability without breaking intended functionality. DARPA’s scoring emphasis on patching illustrates why remediation is a separate measure from discovery.
5. Disclose through an authorized channel
Researchers should contact the maintainer or vendor using its security-reporting process and share enough information to support validation and repair. OpenAI’s June 2025 policy describes private vendor contact by default and an open-ended disclosure timeline by default, while reserving the option to disclose in some circumstances, such as public interest. That is OpenAI’s policy, not a universal industry deadline or rule.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you judge claims about AI vulnerability discovery?
Do not compare unlike benchmark percentages as if they were entries on one leaderboard. A result on known or synthetically seeded flaws does not by itself show how often a system finds real, previously unknown vulnerabilities in ordinary production software.
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| Evaluation question | What to look for |
|---|---|
| Discovery | Was the system tested on known flaws, seeded flaws, or previously unknown issues? What software and codebase conditions were involved? |
| Validation | Could it reproduce the suspected issue in an isolated environment and provide evidence a reviewer can inspect? |
| Severity and reporting | Does the report explain affected components, triggering conditions, and impact clearly enough for security professionals to assess? |
| Remediation | Can it propose a repair, and was that repair tested to preserve intended behavior? |
| Test conditions | Which model, tools, access tier, safeguards, benchmark, and evaluation limits were used? |
| Governance | Was testing authorized, and were findings handled through responsible or coordinated disclosure? |
The cited results do not establish an independently replicated, cross-vendor success rate for finding real zero-days across software. OpenAI’s reports are company-reported results under described access and evaluation conditions; DARPA’s figures come from a competition. Those are useful evidence of capability, but they are not interchangeable measures.
Can anyone use a public chatbot to find zero-days?
Not necessarily. Reported capabilities do not mean every user can reproduce the results in a public chatbot or a default configuration. OpenAI’s Daybreak announcement describes separate Blue access for approved defensive work and Red access for authorized vulnerability research, exploit validation, and security testing. The company’s Astra update specifically says its reported results reflected Blue access rather than the default production configuration.
OpenAI also describes access restrictions and safeguards for advanced cyber capabilities. In the Astra report, it cautions that enhanced checks can slow, pause, or stop legitimate work. Availability and results therefore depend on the system, access tier, task, and safeguards in use.
What should a security team do with an AI-generated finding?
- Confirm that the assessment is authorized and stays within the approved scope.
- Reproduce the suspected issue in an isolated test environment rather than probing systems without permission.
- Have a security professional review the evidence, conditions, and likely impact before assigning severity.
- Test proposed fixes and check that intended software behavior remains intact.
- Report the validated issue to the maintainer or vendor through its security process and coordinate disclosure appropriately.
Finding a vulnerability does not authorize testing systems that you do not own or have permission to assess. Keep testing to software and environments for which you have explicit authorization.
