Acunetix
Web vulnerability scanner for automated security testing of websites and web apps
Vulnhuntr
AI-driven static analysis uncovers remote exploit chains in Python code
Vulnhuntr uses large language models to automatically trace user input through Python call chains, revealing complex remotely exploitable vulnerabilities beyond traditional static analysis.
Overview
Overview
Vulnhuntr empowers security engineers and developers to discover remotely exploitable bugs in Python applications without writing custom test cases. By prompting a large language model to follow the flow from user‑controlled input to server‑side processing, the tool builds complete call‑chains and surfaces multi‑step vulnerabilities that static scanners typically miss.
Capabilities & Deployment
The system supports Claude, OpenAI GPT, and Ollama (experimental) as back‑ends, generating a detailed report that includes reasoning, a proof‑of‑concept exploit, and a confidence score. Installation is straightforward via Docker, pipx, or Poetry, but requires Python 3.10 due to parser dependencies. Users supply an API key, point the CLI at a local repository, and optionally narrow the analysis to specific files handling external input.
Who Benefits
Security auditors, DevSecOps teams, and open‑source maintainers can integrate Vulnhuntr into CI pipelines or run it ad‑hoc to prioritize remediation of high‑confidence findings such as RCE, XSS, SSRF, IDOR, and more.
Highlights
LLM‑driven call‑chain analysis to uncover multi‑step vulnerabilities
Automatic proof‑of‑concept generation with confidence scoring
Supports Claude, OpenAI GPT, and experimental Ollama back‑ends
One‑click Docker or pipx installation for Python 3.10 environments
Pros
- Finds complex remote‑exploitation paths missed by traditional scanners
- Provides actionable PoC and confidence metric
- Flexible LLM provider selection
- Easy containerized or pipx deployment
Considerations
- Limited to Python codebases
- Requires Python 3.10, incompatible with other versions
- LLM usage can incur significant API costs
- Support for open‑source LLMs is currently experimental
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Fit guide
Great for
- Security researchers hunting zero‑day bugs in Python projects
- DevSecOps teams adding AI‑assisted scans to CI pipelines
- Open‑source maintainers auditing third‑party dependencies
- Penetration testers needing rapid vulnerability proof‑of‑concepts
Not ideal when
- Projects written in languages other than Python
- Organizations without budget for LLM API usage
- Environments that cannot upgrade to Python 3.10
- Teams requiring fully deterministic, rule‑based analysis only
How teams use it
Automated security audit of a new Python web framework
Identified hidden RCE and XSS vectors, enabling developers to patch before release
CI integration for continuous vulnerability monitoring
Runs nightly scans, flags high‑confidence findings, and generates PoC for rapid triage
Bug bounty verification for reported exploits
Reproduces reported issues with AI‑generated PoC, confirming severity and scope
Open‑source dependency review before inclusion
Detects remote‑code execution risks in third‑party libraries, informing safe adoption decisions
Tech snapshot
Python100%
Dockerfile1%
Frequently asked questions
Which programming languages does Vulnhuntr support?
Only Python codebases are currently supported.
What LLM providers can be used?
Claude (default), OpenAI GPT models, and Ollama (experimental) are supported.
Do I need an API key to run the tool?
Yes, an API key for the chosen LLM service must be set in the environment.
Can I limit the analysis to specific files?
Yes, use the `-a` option to target a particular file or subdirectory.
How does the confidence score work?
Scores below 7 indicate low likelihood, 7 suggests investigation, and 8+ signals a strong probability of a real vulnerability.
Project at a glance
Stable- Stars
- 2,455
- Watchers
- 2,455
- Forks
- 278
LicenseAGPL-3.0
Repo age1 year old
Last commit11 months ago
Primary languagePython
Last synced 3 hours ago