Yes. AI can help defenders spot and explain candidate vulnerabilities, prioritize code for review, and suggest fixes. It cannot reliably prove a vulnerability is real or a patch is safe on its own. The safer approach is to use AI within an authorized defensive workflow, verify findings with established analysis and human review, and handle any third-party vulnerability through responsible disclosure. Because the same capabilities can be misused, no workflow can guarantee that AI-assisted security work has zero dual-use risk.
What AI can contribute to vulnerability detection
AI can assist with tasks such as interpreting a code-security warning, identifying code that deserves closer review, and proposing a possible remediation. It can also help defenders work through findings in the context of a codebase. These capabilities are most useful as an additional layer around software security practices—not as a replacement for static and dynamic analysis, tests, source review, or security expertise.
GitHub documents AI-assisted features in its security and quality workflows. For example, CodeQL findings can receive Copilot Autofix suggestions, and secret scanning includes generic secret detection. These are examples of vendor-described capabilities, not evidence that one product outperforms another or that every suggestion is correct. GitHub advises users: “Always review suggestions before accepting: Evaluate the proposed code change to ensure it correctly fixes the security vulnerability without changing the intended behavior of your code.”
Why an AI finding needs verification
A model can flag code that is not vulnerable, miss a real issue, or give an explanation that sounds convincing but does not hold up under examination. It may also produce different answers when asked to analyze the same code again. A warning is therefore a lead to investigate, not proof of exploitability; a suggested patch is a candidate change, not a verified fix.
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Published evaluations illustrate these limits, but their results apply to the models, tools, and methods tested—not to every current AI system or deployment.
| Evidence | What was evaluated or reported | How to interpret it |
|---|---|---|
| 2024 IEEE Symposium on Security and Privacy paper, LLMs Cannot Reliably Identify and Reason About Security Vulnerabilities (Yet?) | 228 code scenarios; the paper reports high false-positive rates, answers that changed across repeated runs, and questionable reasoning even when a model identified a vulnerability. | Evidence about the evaluated models and test design, not a universal error rate for AI vulnerability detection. |
| 2026 arXiv preprint, LLM-based Vulnerability Detection at Project Scale: An Empirical Study | The study reports a benchmark of 222 known real-world vulnerabilities and a manual analysis of 385 warnings across 24 active open-source projects. It reports high false discovery rates in the reviewed warnings from both LLM-based and traditional tools. | A preprint with results limited to the tools and projects studied; its sample sizes are not population estimates. |
| Google Project Zero, “Project Naptime: Evaluating Offensive Security Capabilities of Large Language Models” (June 2024) | Project Zero reported up to a 20-fold improvement on the CyberSecEval2 benchmark after refining its testing methodology. | A benchmark-specific comparison tied to that setup—not evidence of a 20-fold increase in real-world vulnerability discovery. |
These findings also show why a single benchmark score is not enough to rank tools. Results can depend on the model, prompt, code context, test set, and workflow used to check outputs. Meta AI’s CyberSecEval 2 suite explicitly includes evaluating LLMs’ ability to automate software vulnerability exploitation, underscoring that security capabilities can be dual-use.
A safer workflow for using AI in defense
- Set authorization and scope. Analyze only code and systems you are permitted to assess. Limit access to the relevant repositories and environments, and apply your organization’s rules for handling sensitive code and findings.
- Use AI to prioritize or explain leads. Ask it to help interpret a warning or identify code that merits review. Do not treat a model’s confidence or fluent explanation as confirmation that a vulnerability exists.
- Corroborate the issue. Review the relevant source and use established static or dynamic analysis, tests, and reproducible evidence appropriate to the risk. If the finding cannot be confirmed, record the uncertainty rather than presenting it as established.
- Validate the proposed change. Review the patch for whether it addresses the underlying issue, preserves intended behavior, and avoids regressions or new findings. Run the project’s relevant tests and security checks before accepting it.
- Handle third-party findings carefully. Follow the affected project’s security policy. Where appropriate, report privately and coordinate with maintainers before public disclosure. GitHub’s guidance on coordinated disclosure describes reporting as collaboration between reporters and maintainers, with details ideally published after remediation or a patch.
How to assess an AI security tool
Evaluate a tool in the context of your own code and review process rather than relying on one headline score. Consider:
- Coverage: Which languages, vulnerability classes, and kinds of project context does it support?
- Finding quality: How many warnings can reviewers confirm, and how much effort does false-positive triage create?
- Reproducibility: Do repeated runs produce stable findings and explanations?
- Workflow fit: Can its output be checked against deterministic scanners, tests, and source review?
- Remediation quality: Do proposed changes address the issue while preserving intended behavior and avoiding regressions?
- Access and disclosure controls: Can you scope what the tool can access and protect sensitive code and findings?
For security learning and defensive practice, GitHub Security Lab publishes materials that include remediation-focused guidance, GitHub-native workflows, and CI/CD hardening. Such guidance can support a defensive program, but it does not remove the need to validate findings and changes in the project being assessed.
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Can AI find zero-day vulnerabilities?
AI may help surface a previously unknown vulnerability, but the evidence summarized here does not establish that current AI systems can autonomously and reliably discover zero-days across real-world software. A candidate finding still needs confirmation, and any discovery in software you do not own or administer should be handled within the relevant authorization and disclosure process.
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