ASL V6 vs Panguard.AI

ASL V6 and Panguard.AI are both guardrails & safety tracked by AIDiveForge. Below is a side-by-side comparison of pricing, capabilities, platforms, and ownership — sourced from each tool's live website and verified before publishing.

ASL V6

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.

Panguard.AI

Panguard installs in one command, runs entirely offline with zero telemetry, and auto-detects agents across a wide surface — Claude Code, Cursor, VS Code Copilot, Gemini CLI, and more. The vendor states 768 ATR (Agent Threat Rules) execute locally as deterministic checks before any skill loads, then continue guarding each action at runtime against prompt injection and poisoned MCP tools. Rules contributed anywhere benefit every adopter — the vendor describes this as 'threat crystallization.' The ceiling appears when a threat is genuinely novel: deterministic rules only catch what someone has already seen and codified, so the AI analysis fallback carries the weight for zero-day patterns. Teams with regulated environments get signed, audit-ready output without routing data to a third party.

Attribute	ASL V6	Panguard.AI
Pricing	Free	Free
Free trial	No	No
Open source	Yes	Yes
Has API	No	No
Self-hosted option	Yes	Yes
Platforms	Linux, macOS, Windows (with Docker)	Linux, macOS (via shell install)
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.	One-command offline install with zero telemetry, which means teams in air-gapped or regulated environments get runtime protection without routing agent traffic through a third-party service. 768 deterministic ATR rules execute locally in milliseconds, so security checks add no meaningful latency to skill loading and produce consistent, reproducible results rather than probabilistic LLM verdicts. Community threat corpus with upstream merges from Cisco and Microsoft, which means a rule written against an attack anywhere in the ecosystem closes the same gap for your agents without your team having to discover the threat independently. Signed, audit-ready output generated locally, so compliance reviews have a tamper-evident evidence trail without exporting agent behavior data to a vendor. Auto-detects a broad set of agent environments — Claude Code, Cursor, VS Code Copilot, Gemini CLI, and more — so teams running heterogeneous tooling do not need per-environment configuration to get baseline coverage.
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.	Deterministic rules only catch threats someone has already seen and codified: a novel prompt injection technique or a newly poisoned MCP tool with no prior CVE or ATR entry passes the rule layer clean. The AI analysis fallback carries that burden, but teams whose threat model is dominated by zero-day or highly targeted attacks are betting on a layer with no published recall figures for unseen patterns. No API and no hosted option, which means security checks cannot be integrated into a CI pipeline or a centralized policy enforcement layer without scripting around the CLI directly — teams that need programmatic gate control in their build system end up writing and maintaining that wrapper themselves. Private, organization-specific tooling generates attack surfaces the community corpus will never describe. Teams building internal MCP servers with custom business logic will need to author their own ATR rules, and the docs describe a review-and-merge pipeline optimized for community contribution — not private rule management at scale. At the point where a team is maintaining a significant private rule library on top of the public corpus, the operational model starts to resemble a full detection engineering practice, and teams with that capacity often move toward purpose-built security platforms that offer rule management, alerting, and incident workflows.

Bottom line

ASL V6 and Panguard.AI are closely matched on pricing model, openness, and API availability — pick by feature set and platform support in the table above.

Frequently asked questions

What is the difference between ASL V6 and Panguard.AI?

ASL V6 is Free and open source, while Panguard.AI is Free and open source. Compare pricing, free trial, API, platforms, and pros/cons in the table above on AIDiveForge.

Is ASL V6 better than Panguard.AI?

It depends on your workflow. Use the side-by-side attributes (pricing, open source, API, self-hosted, platforms) to decide. AIDiveForge does not rank a universal winner — we publish verified facts so you can choose.