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ASL V6
FreeOpen SourceSelf-Hosted
Static analysis tools flag too much noise, dynamic scanners miss AI-specific attack surfaces, and most security tooling was built before prompt injection existed as a threat class — ASL V6 was written to close that gap for Python AI codebases.
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Get This Tool
License: MIT Any use incl. commercial
Local-run terms: MIT license permits commercial use, modification, distribution, and private use with attribution required.
Pricing
- Model
- Free
Summary
ASL V6 combines AST-based static analysis with Docker-isolated runtime verification to find and confirm exploitable vulnerabilities in AI agent frameworks before they ship. The dual-layer approach means a finding isn't just flagged — it's verified in a sandboxed execution environment, which cuts the false-positive rate that burns security team time. It runs entirely offline with no external API calls, so sensitive proprietary code never leaves your machine. The ceiling appears quickly on non-Python codebases and on teams that need ticketing integrations or cloud-native CI pipelines baked in rather than assembled by hand.
Bottom line: The right pick for a security researcher auditing an open-source AI framework on a local machine; a poor fit for a team that needs multi-language coverage or an out-of-the-box integration with their existing vulnerability management platform.
Find your fit
Before you choose ASL V6
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Pros
- AST static analysis paired with Docker runtime verification confirms exploitability before surfacing a finding, so your team spends time fixing real vulnerabilities rather than triaging false positives.
- 100% local execution with no external API calls, which means auditing proprietary or pre-release AI code without the legal and compliance risk of sending source to a third-party service.
- Remediation patch generation alongside confirmed findings, so developers receive an actionable fix rather than a vulnerability description they have to decode into a code change.
- MIT license with self-hosted deployment, so security teams can run it inside air-gapped environments or modify detection rules to match their specific AI framework stack without vendor approval.
- Detection rules derived from confirmed, disclosed CVEs in production AI systems (AutoGPT, FlowiseAI), which means the tool targets attack patterns that have already caused real damage rather than theoretical edge cases.
Cons
- Coverage is scoped entirely to Python — teams auditing AI systems with Node.js tool-calling layers, Go-based infrastructure, or polyglot agent frameworks get no static or dynamic analysis for the non-Python surface, and there is no documented path to extend language support without forking the project.
- Docker is a hard runtime dependency for the dynamic verification layer; teams in environments where Docker is restricted by policy (common in enterprise security tooling reviews) lose the exploit-confirmation step entirely and fall back to static-only output, which is where false positives return.
- There is no native integration with vulnerability management platforms, ticketing systems, or SIEM pipelines — teams that need findings routed into Jira, Defect Dojo, or Splunk build that plumbing themselves, and when the integration maintenance cost grows, teams with existing platform investments switch to commercial SAST tools that ship those connectors out of the box.
About
- Platforms
- Linux, macOS, Windows (with Docker)
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- July 28, 2026
Best For
Who it's for
- Security researchers auditing Python AI code
- Developers integrating AI agent frameworks
- Teams seeking low false-positive vulnerability detection
- Local, offline security analysis without external API calls
What it does well
- Scanning AI agent codebases for prompt injection and tool abuse
- Verifying exploitability of detected vulnerabilities via isolated runtime tests
- Red-teaming open-source AI frameworks before deployment
- Generating remediation patches for identified security issues
Frequently Asked Questions
- Is ASL V6 free?
- ASL V6 offers a free option. Hosting, model usage, or optional services may have separate costs; check the pricing details and official terms.
- Is ASL V6 open source?
- Yes. ASL V6 is open source.
- Can I self-host ASL V6?
- Yes. ASL V6 supports self-hosting on your own infrastructure.
- What platforms does ASL V6 support?
- ASL V6 is available on: Linux, macOS, Windows (with Docker).
Curated lists that include this category
ASL V6 performs vulnerability assessment and red-teaming against AI agent codebases by pairing AST (Abstract Syntax Tree) static analysis with Docker-sandboxed dynamic verification. The static pass walks the Python source for patterns associated with prompt injection, tool abuse, secrets exposure, and infrastructure misconfigurations. When a candidate vulnerability surfaces, the dynamic sandbox re-executes the relevant code path in isolation to confirm exploitability — separating real issues from theoretical ones before the finding reaches a developer.
The distinguishing feature is exploit verification, not just detection. The docs describe a specialist-agent layer (v6_specialist_agents.py) and an advisory engine (v6_advisory_engine.py) that together generate remediation patches alongside confirmed findings. That means the output isn’t a raw list of CWEs — it’s a confirmed issue with a proposed fix attached, reducing the triage and remediation cycle for small security teams.
ASL V6 fits tightly into a specific workflow: local, offline security review of Python AI code, especially open-source frameworks that handle tool invocations or LLM API calls. The author’s track record includes confirmed CVEs against AutoGPT and FlowiseAI, which grounds the detection rules in real exploit patterns rather than generic SAST heuristics. Where it breaks down is outside that scope — non-Python AI code gets no coverage, and teams running cloud-based CI/CD pipelines will need to wire up their own integration using the provided GitHub Actions YAML rather than connecting to a managed platform.
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