Droid LLM Hunter, an open-source tool for detecting vulnerabilities in Android apps, has been released on GitHub . It combines traditional static analysis (SAST) with large language models (LLMs) to interpret code in context and narrow findings down to verified, high-severity issues.
Open Source · Mobile Security Tooling
Droid LLM Hunter pairs static analysis with LLMs to hunt Android app flaws
A newly released open-source scanner blends traditional SAST with large-language-model reasoning to cut false positives, auto-generate proof-of-concept exploits, and slot into CI/CD as a quality gate.
25
Detection rules, mapped to OWASP MASVS
6
Supported LLM providers
v1.1.8
Latest release · 14 releases in total
149
GitHub stars · 27 forks at release
The hybrid pipeline
Four stages take an APK from raw bytes to an actionable, low-noise report.
01 · HYBRID DECOMPILE
APK into both Smali and Java
→
02 · CONTEXT-AWARE
Injects call-graph context
→
03 · RISK FILTER
Suppresses false alarms
→
04 · AUTO-EXPLOIT
Generates PoC scripts
Why hybrid over keyword-only
Conventional tools like APKTool and Jadx match on keywords, flooding reports with false positives. Adding an LLM's contextual reasoning approximates human code review — reducing noise while improving accuracy. Bug-bounty use has already surfaced a flaw in a Google Android app.
A caution from the developer
"LLMs are probabilistic and can hallucinate or misfire, so manual verification is recommended." It is framed as a tool to augment — not replace — human analysis. Hands-on user accounts remain limited so far.
Fits the workflow
CLI & GitHub Action
Run python dlh.py scan target.apk . In CI/CD it returns exit code 1 on findings, acting as a quality gate. Flags: --generate-exploit , --rules .
Roadmap to v2.0
Dynamic analysis via Frida/ADB, taint analysis, and deobfuscation support. A vulnerable test app and a results dashboard ship alongside.
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