We found 24 Android vulnerabilities using our open source AI security agent
GitHub Security Lab researchers utilized an open-source AI agent to identify 24 vulnerabilities in Android applications. The team developed specific taskflows to guide large language models in detecting complex security flaws that might otherwise be missed. The article details the methodology and provides instructions for security researchers to run these audits on their own projects.
- ▪Kevin Stubbings and his team created the GitHub Security Lab Taskflow Agent to automate and share effective AI security workflows.
- ▪Custom taskflow prompts allow researchers to guide LLMs through incremental steps to find complex Android vulnerabilities faster.
- ▪Running the taskflows requires a GitHub Copilot license and may consume a large amount of premium model tokens.
- ▪Specific taskflows were added to identify mobile entry points and classify application vulnerabilities like confused deputy or insecure broadcasts.
- ▪The AI agent successfully identified vulnerabilities in popular apps such as OsmAnd, including a flaw that allowed malicious apps to track device location.
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| Original publisher | The GitHub Blog |
| Canonical URL | https://github.blog/security/how-we-found-24-android-vulnerabilities-using-our-open-source-ai-security-agent/ |
| Publication time | Tue, 29 Sep 2026 00:55:47 +0000 |
| Retrieval time | 2026-09-29T01:13:03.685Z |
| Last seen | 2026-09-29T01:13:03.685Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | Qdz3H79qBz-X · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Home / Security How we found 24 Android vulnerabilities using our open source AI security agent A look at the targeted AI taskflows behind these findings, the critical Android bugs they uncovered, and how to run the same open-source agent on your own app. Kevin Stubbings·@kwstubbs September 28, 2026 | 10 minutes Share: With the rise of AI in the security space, our team created the GitHub Security Lab Taskflow Agent as a way for security researchers to easily automate, package, and share the AI prompts and workflows that they find effective for their work. In this blog post, I’ll share how I created auditing taskflows to find vulnerabilities in Android applications.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at The GitHub Blog.
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