🔷 Prism Scanner
Security scanner for AI Agent skills, plugins, and MCP servers.
<!-- mcp-name: io.github.aidongise-cell/prism-scanner -->Prism analyzes code for malicious behavior before you install it — and checks your system for leftover threats after you uninstall.
Unlike marketplace-only trust scores, Prism gives you full lifecycle coverage with code-level transparency — pre-install, runtime, and post-uninstall — across every platform, completely open source.
✨ What's new in v0.2.x
- P10 — Agent psychological manipulation detection: detects gaslighting, guilt-tripping, authority impersonation, urgency pressure, and emotional coercion patterns embedded in skill descriptions, prompts, and code strings. Based on Northeastern/Harvard/MIT research on conversational manipulation of AI agents — to our knowledge, the first open-source automated detector for this attack class.
- M7 — Publish hygiene checks: catches source maps,
.envfiles, private keys, IDE configs, and package-manager credentials before they ship. Inspired by Anthropic's April 2026 source-map leak that exposed 512K lines of Claude Code internals via npm. Wire it into your CI:prism scan ./dist --fail-on high - 47–93% fewer false positives on real-world skills (S1, S4, S8, S12 engine fixes; validated against 3 representative ClawHub skills that previously flooded with false flags).
- 41 detection rules total across S1–S14 (behavior), M1–M7 (metadata + publish hygiene), P1–P10 (patterns + manipulation), and R1–R10 (residue).
- v0.2.2 fixes a cosmetic version-string mismatch (
prism --versionreported0.1.3after upgrading to0.2.1); detection behavior is identical to v0.2.1.
Why Prism?
| Marketplace Trust Scores | Prism Scanner | |
|---|---|---|
| Pre-install | ✅ Reputation score | ✅ Deep code analysis |
| Post-uninstall | ❌ | ✅ Residue & persistence scan |
| Inspection | Black-box rating | Code-level, rule-by-rule |
| Platforms | Single ecosystem | ClawHub, MCP, npm, pip |
| Source | Closed | Open (Apache 2.0) |
| Execution | Requires upload | Local-first, offline OK |
Quick Start
pip install prism-scanner
# Scan a local skill directory
prism scan ./my-skill/
# Scan a GitHub repo directly
prism scan https://github.com/user/skill-repo
# Check your system for agent residue
prism clean --scan
# Generate a cleanup plan (non-destructive)
prism clean --plan
# Execute cleanup with automatic backups
prism clean --apply
Homebrew (macOS)
brew tap prismlab/tools
brew install prism-scanner
npx (no install needed)
npx prism-scanner scan https://github.com/user/skill-repo
GitHub Action (CI/CD)
Add Prism to your CI pipeline — findings appear in GitHub's Security tab:
# .github/workflows/prism-scan.yml
name: Prism Security Scan
on: [push, pull_request]
permissions:
security-events: write
contents: read
jobs:
scan:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- uses: aidongise-cell/prism-scanner@main
with:
path: '.'
fail-on: 'high'
MCP Server (Claude Desktop / Cursor / VS Code)
Prism Scanner can run as an MCP server, giving AI assistants direct access to security scanning tools.
pip install "prism-scanner[mcp]"
Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json):
{
"mcpServers": {
"prism-scanner": {
"command": "prism-mcp"
}
}
}
Or for Claude Code:
claude mcp add prism-scanner prism-mcp
This exposes 4 tools: prism_scan, prism_grade, prism_clean_scan, prism_clean_plan.
Docker
# Build the image
docker build -t prism-scanner .
# Scan a local directory
docker run -v $(pwd)/my-skill:/workspace/target:ro prism-scanner scan /workspace/target
# Scan a remote repo
docker run prism-scanner scan https://github.com/user/skill-repo
# Generate HTML report
docker run -v $(pwd):/workspace/output prism-scanner scan https://github.com/user/repo --format html -o /workspace/output/report.html
Or use the published image:
docker run ghcr.io/prismlab/prism-scanner scan <target>
What It Detects
Prism operates across 3 analysis layers, each targeting a different phase of the agent lifecycle:
| Layer | Focus | Rules | Examples |
|---|---|---|---|
| 1. Code Behavior (S1-S14) | What the code does | 14 | Shell execution, data exfiltration, SSRF, persistence mechanisms, unsafe deserialization, download-and-execute |
| 2. Metadata (M1-M6, P1-P9) | What the package claims vs. contains | 15 | Hardcoded credentials, typo-squatting, install scripts, obfuscated payloads, prompt injection, suspicious domains |
| 3. System Residue (R1-R10) | What was left behind | 10 | LaunchAgents, crontab entries, shell config pollution, orphaned credentials, systemd units, login items |
Total: 39 detection rules with lightweight intra-file taint analysis.
Example Output
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Prism Scanner v0.1.0
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Target: evil_skill
Platform: clawhub
Duration: 42ms
[1/3] Behavior Analysis
✗ CRITICAL S8 Data exfiltration: secrets sent to external endpoint evil_skill.py:10
✗ CRITICAL S10 Download and execute: fetches remote payload evil_skill.py:24
✗ HIGH S1 Shell execution with untrusted input evil_skill.py:14
✗ HIGH S13 Persistence: writes LaunchAgent plist evil_skill.py:36
⚠ MEDIUM S6 Dynamic code execution (eval/exec) evil_skill.py:21
[2/3] Metadata Analysis
✗ CRITICAL P1 Hardcoded credential: AWS Access Key evil_skill.py:47
⚠ MEDIUM P2 Base64-encoded executable content evil_skill.py:50
⚠ MEDIUM P6 Prompt injection pattern in string literal evil_skill.py:53
[3/3] Residue Scan
(skipped — use `prism clean --scan` for system-level checks)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Grade: F (Critical)
Key Risks:
▸ CRITICAL: Data exfiltration to external endpoint (S8)
▸ CRITICAL: Download and execute remote payload (S10)
▸ CRITICAL: Hardcoded AWS credential (P1)
▸ HIGH: Shell execution with user input (S1)
▸ HIGH: Persistence mechanism installed (S13)
Behavior Profile:
exfiltrates_data, executes_shell, downloads_and_executes, installs_persistence
Recommendation: DO NOT INSTALL — Critical security risks detected.
Findings: 3 critical, 2 high, 3 medium
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Grading System
| Grade | Label | Meaning | Recommendation |
|---|---|---|---|
| A | Safe | No findings or only informational | Safe to use |
| B | Notice | Only LOW severity findings | Likely safe — minor observations |
| C | Caution | 1-4 MEDIUM findings | Review before use — warrants manual inspection |
| D | Danger | 1-2 HIGH, or 5+ MEDIUM | Use in sandbox only — significant risks |
| F | Critical | Any CRITICAL, or 3+ HIGH | Do not install — critical security risks |
Output Formats
# Rich terminal output (default) — color-coded severity, grade highlighting
prism scan ./skill/
# Machine-readable JSON
prism scan ./skill/ --format json
# Standalone HTML report — dark-themed, self-contained, shareable
prism scan ./skill/ --format html -o report.html
# SARIF for GitHub Code Scanning
prism scan ./skill/ --format sarif -o results.sarif
HTML Report
Generate a standalone, self-contained HTML report with full findings, grade visualization, and risk breakdown — perfect for sharing with teammates or archiving:
prism scan https://github.com/user/skill-repo --format html -o report.html
The HTML report features a dark-themed UI with color-coded severity levels, an interactive grade ring, and a complete findings table. No external dependencies — just open the .html file in any browser.
CI/CD Integration
Add Prism to your GitHub Actions workflow to gate deployments on security findings:
name: Security Scan
on: [push, pull_request]
jobs:
prism-scan:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Set up Python
uses: actions/setup-python@v5
with:
python-version: "3.12"
- name: Install Prism Scanner
run: pip install prism-scanner
- name: Run security scan
run: prism scan . --format sarif -o results.sarif --fail-on high
- name: Upload SARIF results
if: always()
uses: github/codeql-action/upload-sarif@v3
with:
sarif_file: results.sarif
The --fail-on flag exits with code 1 if any finding meets or exceeds the specified severity (critical, high, or medium), failing the CI pipeline.
Key Features
- Taint Analysis — Tracks data flow from sources (env vars, user input) to sinks (shell, network) within each file
- Multi-Platform — Scans ClawHub skills, MCP servers, npm packages, and pip packages with platform-aware rules
- Zero Dependencies on Target — Pure static analysis; never executes scanned code
- Residue Scanner — Detects persistence mechanisms, shell config pollution, and credential leaks left by uninstalled tools
- Safe Cleanup — Three-tier workflow (
scan->plan->apply) with automatic backup and--rollback - Suppression — Use
.prismignoreto suppress known findings by rule ID with justification - Offline Mode — Run with
--offlineto skip all external lookups
Adding Custom Rules
Detection rules are defined in YAML files under the rules/ directory:
rules/
├── malicious_signatures.yaml # Known malicious code signatures and hashes
├── permissions.yaml # Permission baseline definitions
└── suspicious_domains.yaml # C2 domains, dynamic DNS, disposable TLDs
The pattern engine (P1-P9) and manifest engine (M1-M6) load rules from these files at scan time. Add entries to extend detection without modifying Python code.
Project Structure
src/prism/
├── cli.py # CLI entry point and output formatting
├── scanner.py # Orchestrator — runs engines, collects findings
├── models.py # Finding, ScanResult, Severity, Layer data models
├── scoring.py # Letter-grade risk assessment (A-F)
├── report.py # HTML report generator
├── fetcher.py # Git clone / URL fetching with security guards
├── cleaner.py # System cleanup: plan, apply, rollback
├── suppression.py # .prismignore parsing
├── rules_loader.py # YAML rule loading
└── engines/
├── ast_engine.py # AST-based analysis (S1-S14)
├── pattern_engine.py # Regex pattern matching (P1-P9)
├── manifest_engine.py # Metadata & manifest analysis (M1-M6)
├── residue_engine.py # System residue scanner (R1-R10)
└── taint.py # Intra-file taint tracking
Contributing
Contributions are welcome. Please see CONTRIBUTING.md for guidelines on:
- Adding new detection rules
- Writing and running tests
- Code style and commit conventions
License
Apache License 2.0 — free for commercial and personal use.
Prism Verified Badge
Show that your project has been scanned by Prism. Add a badge to your README:
Grade A (Safe):
[](https://github.com/aidongise-cell/prism-scanner)
All grades:
| Grade | Badge |
|---|---|
| A |  |
| B |  |
| C |  |
| D |  |
| F |  |
Preview:
Support
If Prism Scanner helped you catch a security risk or gave you peace of mind, please consider giving it a ⭐ on GitHub — it helps others discover the project and keeps development going.
Acknowledgments
Prism Scanner is developed by Prism Lab to address a gap in the AI agent ecosystem: the lack of transparent, code-level security tooling that works across platforms and covers the full agent lifecycle. We believe developers deserve to understand exactly what a skill or plugin does before trusting it with their system.