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Panguard.AI
FreeOpen SourceSelf-Hosted
Every team building with AI agents is reinventing detection from scratch — no shared CVE vocabulary, no Sigma-style rulebook, just private threat lists that fall behind the next attack. Panguard AI exists to close that gap with a deterministic, community-sourced rulebook for what agents must never do.
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Get This Tool
License: MIT Any use incl. commercial
Local-run terms: MIT license permits commercial use, modification, and redistribution with attribution; runs fully offline with no telemetry.
Screenshots 1
Pricing
- Model
- Free
Summary
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.
Bottom line: Panguard is the right call for teams that need deterministic, offline security checks before shipping an agent into production — but if your threat model is dominated by novel, organization-specific attack patterns that no community rule will ever describe, the 768-rule corpus is a floor, not a ceiling.
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Pros
- 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
- 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.
About
- Platforms
- Linux, macOS (via shell install)
- API Available
- No
- Self-Hosted
- Yes
- Last Updated
- July 20, 2026
Best For
Who it's for
- Developers building or running AI agents
- Teams needing deterministic local security checks
- Organizations requiring audit trails for agent behavior
What it does well
- Scan MCP skills for security risks before loading
- Guard agent actions at runtime against prompt injection and poisoned tools
- Generate audit-ready evidence for compliance
- Contribute and consume community threat rules
Integrations
Frequently Asked Questions
- Is Panguard.AI free?
- Panguard.AI offers a free option. Hosting, model usage, or optional services may have separate costs; check the pricing details and official terms.
- Is Panguard.AI open source?
- Yes. Panguard.AI is open source.
- Can I self-host Panguard.AI?
- Yes. Panguard.AI supports self-hosting on your own infrastructure.
- What platforms does Panguard.AI support?
- Panguard.AI is available on: Linux, macOS (via shell install).
Curated lists that include this category
When an AI agent reads a malicious instruction buried in a tool response and obeys it, the attack never touched your code. Panguard AI audits every MCP skill and agent action against a library of 768 executable ATR rules — deterministic checks that run locally, before a skill loads and again at runtime. The install is a single curl command with no signup required. The vendor states it auto-detects agents running in Claude Code, Cursor, Windsurf, Gemini CLI, VS Code Copilot, and roughly a dozen other environments.
The differentiating claim is what Panguard calls ‘threat crystallization’: when an attack is caught and understood, it is written once as a deterministic rule and immediately available to every adopter. The vendor cites a roughly one-hour pipeline from new attack to merged rule, compared to weeks or months for traditional Sigma/CVE processes. Rules are MIT-licensed and the corpus includes upstream merges from Cisco and Microsoft. The vendor reports 97.2% recall against the Garak benchmark suite.
Panguard fits teams that need local, air-gapped security checks with a signed audit trail — no data leaves the host, which satisfies compliance requirements that ruled out SaaS-based scanning. The gap appears when a threat has no prior art: deterministic rules cannot catch what has not yet been codified, and the AI analysis fallback is the only coverage for genuinely novel attack patterns. Teams whose primary risk surface is bespoke, internal tooling with attack vectors unique to their environment will find the community corpus less applicable and will need to author and maintain private rules alongside it.
The vendor states the tool has scanned 67,799 MCP skills and found roughly 1.9% carrying CRITICAL or HIGH risk — including real CVEs against Claude Code (CVE-2025-59536, CVE-2026-21852), MCPJam Inspector (CVE-2026-23744), and Azure MCP Server (CVE-2026-26118). The ATR format is described as the CVE/Sigma equivalent layer for agents, and the project is MIT-licensed with no paid tier listed.
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