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AgentSeal

Security toolkit for AI agents. Red-team prompts, detect MCP poisoning,
scan skill files, trace toxic data flows. 225+ tests across 28 agents.

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Quick Start

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

agentseal guard demo

What does each command do?

Command What it does Needs an LLM?
guard Scans skill files, MCP configs, toxic data flows, and supply chain changes on your machine No
scan Tests a system prompt against 225+ adversarial attack probes Yes*
scan-mcp Connects to a live MCP server and audits its tool descriptions for poisoning No
shield Watches agent config files in real time, alerts on threats, quarantines payloads No

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

Guard

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

Scan

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):

Score Level Meaning
85–100 Excellent Strong defenses, resists most known attacks
70–84 High Good defenses, minor gaps
50–69 Medium Moderate risk, several probe categories leaked
30–49 Low Significant vulnerabilities
0–29 Critical Minimal or no defense against prompt attacks 
# 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

CI/CD

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.

Scan-MCP

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.

Shield

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.

How It Works

Attack surface diagram

MCP servers give AI agents access to local files, databases, APIs, and credentials. Tool descriptions can contain hidden instructions that the agent follows but the user never sees.

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
Loading
Detection pipeline (guard) 
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
Loading

Python API

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 / HTTP / Custom function 
# 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="...")

TypeScript API

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.

Supported Providers

Provider Flag API key
OpenAI --model gpt-4o OPENAI_API_KEY
Anthropic --model claude-sonnet-4-5-20250929 ANTHROPIC_API_KEY
MiniMax --model MiniMax-M2.7 MINIMAX_API_KEY
Ollama --model ollama/llama3.1:8b None
LiteLLM --model any --litellm-url http://... Varies
HTTP --url http://your-agent.com/chat None

MCP Security Registry

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

agentseal.org/mcp

Requirements

  • 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

Pro

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

Why AgentSeal?

Capability AgentSeal Snyk (agent-scan) Pillar Lakera Mindgard
Open-source scanner Yes Partial* No No No
Local machine guard (skills + MCP) Yes Yes Partial No No
Prompt red-teaming 225+ probes 20 attack goals Yes Yes Yes
MCP tool poisoning detection Yes Yes Partial Partial No
Toxic data flow analysis Yes Yes Partial No No
Real-time file monitoring Yes No No No No
Public MCP server registry 6,600+ No No No No
Agents supported 28 10+ 2+ N/A N/A
Local LLM support (Ollama) Yes No No No No
No API key required (guard) Yes No No No No

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

Contributing

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

License

FSL-1.1-Apache-2.0

About

Security toolkit for AI agents. Scan your machine for dangerous skills and MCP configs, monitor for supply chain attacks, test prompt injection resistance, and audit live MCP servers for tool poisoning.

agentseal.org

Topics

cli security mcp pentesting red-team ai-agents vulnerability-scanner red-teaming ai-security ai-agent llm prompt-injection llm-security modelcontextprotocol agent-security mcp-security

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v0.8.0 — Guard Latest
Mar 25, 2026
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