Agentseal
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.
Install / Use
/learn @AgentSeal/AgentsealQuality Score
Category
Development & EngineeringSupported Platforms
README
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
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:
- Pattern signatures - known malicious patterns (credential access, exfiltration URLs, shell commands)
- Deobfuscation - decodes Unicode tags, Base64, BiDi overrides, zero-width characters, TR39 confusables
- Semantic analysis - embedding similarity (MiniLM-L6-v2) catches rephrased attacks that bypass patterns
- Baseline tracking - SHA-256 hashes detect config changes since your last scan (rug-pull detection)
- Registry enrichment - live trust scores from the MCP Security Registry (6,600+ servers)
- 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
<details> <summary><strong>Attack surface diagram</strong></summary> <br>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
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
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
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.
