getagentseal/agentseal

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pip install agentseal    # or: npm install agentseal
agentseal guard          # scan your machine - no API key needed

That's it. AgentSeal finds dangerous skill files, poisoned MCP server configs, and data exfiltration paths across every AI agent on your machine.

Want to test a system prompt against adversarial attacks?

agentseal scan --prompt "You are a helpful assistant..." --model ollama/llama3.1:8b  # free, local
agentseal scan --prompt "You are a helpful assistant..." --model gpt-4o              # cloud

*Free with Ollama. Cloud providers (OpenAI, Anthropic, etc.) require an API key.

Scans all AI agent configurations on your machine. No API key, no network calls - everything runs locally.

Supported agents: Claude Code, Claude Desktop, Cursor, Windsurf, VS Code, Gemini CLI, Codex CLI, Cline, Roo Code, Kilo Code, Copilot CLI, Aider, Continue, Zed, Amp, Amazon Q, Junie, Goose, Kiro, OpenCode, OpenClaw, Crush, Qwen Code, Grok CLI, Visual Studio, Kimi CLI, Trae, MaxClaw.

agentseal guard

Guard runs a six-stage detection pipeline on every file it finds:

1. Pattern signatures - known malicious patterns (credential access, exfiltration URLs, shell commands)
2. Deobfuscation - decodes Unicode tags, Base64, BiDi overrides, zero-width characters, TR39 confusables
3. Semantic analysis - embedding similarity (MiniLM-L6-v2) catches rephrased attacks that bypass patterns
4. Baseline tracking - SHA-256 hashes detect config changes since your last scan (rug-pull detection)
5. Registry enrichment - live trust scores from the MCP Security Registry (6,600+ servers)
6. Custom rules - YAML rules to enforce org-specific policies

agentseal guard init             # generate .agentseal.yaml project policy
agentseal guard --output sarif   # SARIF for GitHub Security tab
agentseal guard --output json    # machine-readable output
agentseal guard --no-diff        # skip baseline delta section
agentseal guard test             # validate your custom rules

Tests a system prompt against 225 adversarial attack probes: 82 extraction techniques, 143 injection techniques, and 8 adaptive mutation transforms. Returns a deterministic trust score.

How detection works: Injection probes embed a unique canary string (e.g. SEAL_A1B2C3D4_CONFIRMED). If the canary appears in the response, the probe leaked. Extraction probes use n-gram matching against the ground truth prompt. No LLM judge - same input, same result, every time.

Trust score (0–100):

# OpenAI
agentseal scan --prompt "You are a helpful assistant..." --model gpt-4o

# Anthropic
agentseal scan --prompt "You are a helpful assistant..." --model claude-sonnet-4-5-20250929

# Ollama (free, local)
agentseal scan --prompt "You are a helpful assistant..." --model ollama/llama3.1:8b

# Any HTTP endpoint
agentseal scan --url http://localhost:8080/chat

# From a file
agentseal scan --file ./prompt.txt --model gpt-4o

agentseal scan --file ./prompt.txt --model gpt-4o --min-score 75

Exit code 1 if trust score is below threshold. Use --output sarif for GitHub Security tab integration.

Connects to a live MCP server over stdio or SSE. Enumerates every tool, then runs each description through pattern matching, deobfuscation, semantic similarity, and optional LLM classification. Outputs a trust score per server.

# stdio server
agentseal scan-mcp --server npx @modelcontextprotocol/server-filesystem /tmp

# SSE server
agentseal scan-mcp --sse http://localhost:3001/sse

Catches tool description poisoning - hidden instructions embedded in tool descriptions that make the agent exfiltrate data, execute commands, or override user intent.

Real-time file watcher for agent config paths. Desktop notifications when threats appear. Automatically quarantines files with detected payloads.

pip install agentseal[shield]   # includes watchdog + desktop notification deps
agentseal shield

Monitors the same paths that guard scans, but continuously. Useful for detecting supply chain attacks where an npm install or pip install silently modifies your agent configs.

graph TD
    U["User"] -->|prompt| A["AI Agent (LLM)"]
    A -->|tool call| M1["MCP Server\n(filesystem)"]
    A -->|tool call| M2["MCP Server\n(slack)"]
    A -->|tool call| M3["MCP Server\n(database)"]

    M1 -->|reads| FS["~/.ssh/\n~/.aws/\n~/Documents/"]
    M2 -->|reads| SL["Messages\nChannels"]
    M3 -->|queries| DB["Tables\nCredentials"]

    SL -.->|"toxic flow"| M1
    M1 -.->|"exfiltration"| EX["Attacker"]

    style U fill:#1a1a2e,stroke:#58a6ff,color:#e6edf3
    style A fill:#1a1a2e,stroke:#58a6ff,color:#e6edf3
    style M1 fill:#3b1d0e,stroke:#f59e0b,color:#e6edf3
    style M2 fill:#3b1d0e,stroke:#f59e0b,color:#e6edf3
    style M3 fill:#3b1d0e,stroke:#f59e0b,color:#e6edf3
    style EX fill:#3b0e0e,stroke:#ef4444,color:#e6edf3
    style FS fill:#1a1a2e,stroke:#30363d,color:#8b949e
    style SL fill:#1a1a2e,stroke:#30363d,color:#8b949e
    style DB fill:#1a1a2e,stroke:#30363d,color:#8b949e

graph LR
    IN["Skill Files\nMCP Configs"] --> P["Pattern\nSignatures"]
    P --> D["Deobfuscation\n(Unicode Tags,\nBase64, BiDi,\nZWC, TR39)"]
    D --> S["Semantic\nAnalysis\n(MiniLM-L6-v2)"]
    S --> B["Baseline\nTracking\n(SHA-256)"]
    B --> R["Registry\nEnrichment"]
    R --> RU["Custom\nRules"]
    RU --> OUT["Report +\nSeverity"]

    style IN fill:#1a1a2e,stroke:#58a6ff,color:#e6edf3
    style P fill:#161b22,stroke:#30363d,color:#e6edf3
    style D fill:#161b22,stroke:#30363d,color:#e6edf3
    style S fill:#161b22,stroke:#30363d,color:#e6edf3
    style B fill:#161b22,stroke:#30363d,color:#e6edf3
    style R fill:#161b22,stroke:#30363d,color:#e6edf3
    style RU fill:#161b22,stroke:#30363d,color:#e6edf3
    style OUT fill:#0d4429,stroke:#22c55e,color:#e6edf3

from agentseal import AgentValidator

validator = AgentValidator.from_openai(
    client=openai.AsyncOpenAI(),
    model="gpt-4o",
    system_prompt="You are a helpful assistant...",
)
report = await validator.run()
print(f"Trust score: {report.trust_score}/100 ({report.trust_level})")

# Anthropic
validator = AgentValidator.from_anthropic(
    client=client, model="claude-sonnet-4-5-20250929", system_prompt="..."
)

# HTTP endpoint
validator = AgentValidator.from_endpoint(url="http://localhost:8080/chat")

# Custom function - bring your own agent
validator = AgentValidator(agent_fn=my_agent, ground_truth_prompt="...")

npm install agentseal

import { AgentValidator } from "agentseal";
import OpenAI from "openai";

const validator = AgentValidator.fromOpenAI(new OpenAI(), {
  model: "gpt-4o",
  systemPrompt: "You are a helpful assistant...",
});

const report = await validator.run();
console.log(`Score: ${report.trust_score}/100 (${report.trust_level})`);

The npm package provides the same CLI commands (agentseal guard, scan, scan-mcp, shield) and a programmatic TypeScript API.

6,600+ MCP servers scanned and scored for security risks. Search by name, browse findings, check trust scores before installing.

agentseal.org/mcp

• Python 3.10+ or Node.js 18+
• guard, shield, scan-mcp work offline with no API key
• scan requires an LLM - use Ollama for free local inference, or provide a cloud API key

AgentSeal Pro is for security teams running continuous assessments. It extends the open-source scanner with:

• MCP tool poisoning probes (+45) - rug-pull, shadowing, cross-tool injection
• RAG poisoning probes (+28) - document injection, retrieval manipulation
• Multimodal attack probes (+13) - image prompt injection, audio jailbreaks, steganography
• Behavioral genome mapping - profile how an agent responds across attack dimensions
• PDF reports and dashboard - exportable reports for compliance and stakeholder review

*Snyk agent-scan CLI is Apache-2.0. The Evo platform, Agent Guard, and red-teaming are proprietary SaaS.

Found a detection gap, a false positive, or want to add a new probe? See CONTRIBUTING.md for setup instructions and the PR process.

• Report issues: github.com/AgentSeal/agentseal/issues
• Probe catalog: PROBES.md - full list of all 225 attack probes with techniques and severity

FSL-1.1-Apache-2.0