DeepSafe Scan — Preflight Security Scanner for AI Coding Agents

Full-featured preflight security scanner across 5 dimensions: Posture (config), Skill (skills & MCP), Memory (sessions), Hooks (agent config injection), Model (behavioral safety probes).

Works with OpenClaw, Claude Code, Cursor, and Codex. LLM features auto-detect credentials — no manual configuration needed.

When to Use

• User asks to "scan", "audit", "check security", or "health check" their AI setup
• User installs a new skill, MCP server, or clones a project with agent configs
• User wants to know if any secrets or PII are leaked in session history
• User asks about hooks injection risks (Claude Code settings.json, .cursorrules, etc.)
• User wants to probe model behavior for manipulation, deception, or hallucination risks

How to Run

Quick static scan (no API key needed)

python3 {baseDir}/scripts/scan.py --modules posture,skill,memory,hooks --scan-dir . --no-llm --format markdown

Full scan (auto-detects API credentials)

# OpenClaw (reads gateway config automatically)
python3 {baseDir}/scripts/scan.py --openclaw-root ~/.openclaw --format html --output /tmp/deepsafe-report.html

# Claude Code / Cursor / Codex (uses ANTHROPIC_API_KEY or OPENAI_API_KEY)
python3 {baseDir}/scripts/scan.py --modules posture,skill,memory,hooks,model --scan-dir . --format html --output /tmp/deepsafe-report.html

Targeted scans

# Hooks injection only (fastest — checks .claude/settings.json, .cursorrules, etc.)
python3 {baseDir}/scripts/scan.py --modules hooks --scan-dir . --no-llm --format markdown

# Memory scan only (check for leaked secrets/PII)
python3 {baseDir}/scripts/scan.py --openclaw-root ~/.openclaw --modules memory --no-llm

# Model behavior probes only
python3 {baseDir}/scripts/scan.py --openclaw-root ~/.openclaw --modules model --profile quick

Output options

python3 {baseDir}/scripts/scan.py --format json      # machine-readable
python3 {baseDir}/scripts/scan.py --format markdown  # human-readable summary
python3 {baseDir}/scripts/scan.py --format html --output /tmp/report.html  # visual report

Cache control

python3 {baseDir}/scripts/scan.py --ttl-days 3   # cache for 3 days
python3 {baseDir}/scripts/scan.py --no-cache      # always fresh scan

Interpreting Results

Scores

• Each module scores 1-100 (100 = clean, deductions per finding, minimum 1)
• Module contribution = floor(score / 4), range 1–25
• Total = sum of 4 contributions, max 100

Severity Levels

• CRITICAL (-10 pts): Immediate exploitation risk — secrets exposed, no auth, data exfiltration chains
• HIGH (-5 pts): Serious risk — prompt injection, sensitive file access, network exposure
• MEDIUM (-2 pts): Moderate risk — hardcoded keys, missing logs, supply chain concerns
• LOW (-1 pt): Minor improvement — non-standard endpoints, missing metadata

Risk Ratings

• 85-100: LOW RISK (green)
• 65-84: MEDIUM RISK (yellow)
• 40-64: HIGH RISK (orange)
• 4-39: CRITICAL RISK (red)

After Scanning

1. Present the total score and per-module breakdown to the user
2. List CRITICAL and HIGH findings first with clear explanations
3. For each finding, explain what the risk is and how to fix it
4. Offer to help fix issues (e.g., "want me to add auth to your gateway config?")

What Gets Scanned

Posture Module (deployment config)

Reads

openclaw.json

and checks: gateway auth, network exposure, API key handling, TLS, plugin permissions, MCP server risks, logging, sandboxing.

Skill Module (installed skills & MCP)

Scans all files in

workspace/skills/

,

mcp/

,

mcp-servers/

for: hidden unicode, prompt injection, dangerous runtime calls, encoded payloads, sensitive file references, hardcoded secrets, system prompt extraction, command injection, data exfiltration patterns, destructive actions, auto-execute without confirmation, excessive permissions.

With

--gateway-url

provided (and

--no-llm

not set), also runs LLM-enhanced semantic analysis on each skill's SKILL.md for social engineering, implicit data exfiltration, and deeper pattern recognition.

Memory Module (sessions & stored data)

Scans

agents/

,

credentials/

,

identity/

,

logs/

,

workspace/

for: 27+ secret patterns (API keys for OpenAI, GitHub, AWS, Slack, Stripe, etc.), 9 PII patterns (email, phone, ID numbers, credit cards), persistent prompt injection traces.

Model Module (4 behavioral safety probes)

Requires

--gateway-url

and

--gateway-token

. Runs:

• Persuasion Probe: Tests if the model can manipulate user opinions in multi-turn conversation
• Sandbagging Probe: Tests if the model can strategically hide capabilities (performance control)
• Deception Probe: 3-phase test for reasoning/action misalignment (DTR metric)
• HaluEval Probe: Tests hallucination detection accuracy on QA benchmarks

Each probe produces a finding with risk level and score. Average across probes = module score.