ZT4AI Self-Audit#

Audit your agent's skills, workspace, and configuration against Zero Trust for AI principles.

Background#

AI agents process instructions and data as indistinguishable tokens in a context window. This means:

• Skill files loaded into context can inject behavioral instructions
• Workspace files (SOUL.md, AGENTS.md) are both operating instructions AND attack surface
• External inputs (web content, emails, ClawHub skills) can contain prompt injection
• Credentials in plaintext config files have no access scoping or rotation

This skill applies three frameworks:

1. Microsoft ZT4AI — Verify explicitly, least privilege, assume breach
2. "Caging the Agents" (arXiv:2603.17419) — Four-layer defense: workload isolation, credential proxy, network egress, prompt integrity
3. OWASP Agentic AI Top 10 — Trust boundary violations, privilege escalation, resource exhaustion

Audit Process#

Step 1: Inventory Skills#

Scan all three skill locations:

bash

echo "=== System ===" && ls /usr/lib/node_modules/openclaw/skills/ 2>/dev/null
echo "=== User ===" && ls ~/.openclaw/skills/ 2>/dev/null
echo "=== Workspace ===" && ls ~/.openclaw/workspace/skills/ 2>/dev/null

Step 2: Classify Each Skill#

Assign every skill to a risk category using the classification guide in references/risk-classification.md.

Categories:

• Behavioral modifiers (🔴 highest risk) — Skills that change how you think, override safety instincts, or inject decision-making patterns into your context
• Credential handlers (🟡 elevated risk) — Skills that read, write, or transmit API keys, tokens, passwords
• System modifiers (🟡 elevated risk) — Skills that write to config files, modify system state, or execute with elevated privileges
• Tool wrappers (🟢 standard risk) — Skills that wrap external tools with well-scoped inputs/outputs
• Read-only (🟢 low risk) — Skills that only read data and produce reports

Step 3: Audit Each Skill Against ZT4AI Principles#

For each skill, evaluate against the checklist in references/audit-checklist.md.

Quick reference — the three questions:

1. Verify explicitly: Does this skill verify identity/authorization before acting? Does it distinguish owner from non-owner input?
2. Least privilege: Does this skill request only the access it needs? Could its scope be narrowed?
3. Assume breach: If this skill were compromised (poisoned update, prompt injection in its files), what's the worst outcome? How would you detect it?

Step 4: Check Scripts and Executables#

Find all executable code in skills:

bash

find ~/.openclaw/skills/ ~/.openclaw/workspace/skills/ \
  -type f \( -name "*.sh" -o -name "*.py" -o -name "*.js" \) \
  2>/dev/null | sort

For each script, check:

• Does it access credentials? ( grep -li "API_KEY\|SECRET\|TOKEN\|PASSWORD" <file>)
• Does it make network calls? ( grep -li "curl\|wget\|requests\|fetch\|http" <file>)
• Does it write to system config? ( grep -li "openclaw.json\|\.env\|/etc/" <file>)
• Does it execute arbitrary input? ( grep -li "eval\|exec\|subprocess\|system(" <file>)

Step 5: Generate Integrity Baseline#

Create SHA256 checksums of all skill files for future drift detection:

bash

find ~/.openclaw/skills/ ~/.openclaw/workspace/skills/ \
  -type f \( -name "*.md" -o -name "*.sh" -o -name "*.py" -o -name "*.js" -o -name "*.json" \) \
  -exec sha256sum {} \; | sort -k2 > memory/skill-integrity-baseline.md

To verify against an existing baseline:

bash

sha256sum -c memory/skill-integrity-baseline.md 2>&1 | grep -v ": OK$"

Any output indicates modified files — investigate before trusting.

Step 6: Assess Workspace File Security#

Check the self-modification surface:

• Can the agent modify its own SOUL.md / AGENTS.md? (Yes by default — flag it)
• Are memory files loaded into context? (Yes — they're instruction vectors)
• Is MEMORY.md loaded in non-owner contexts? (Should NOT be — data leak risk)
• Are there credentials in workspace files? ( grep -rli "api_key\|password\|secret" ~/.openclaw/workspace/)

Step 7: Check Network Egress#

Assess outbound network restrictions:

bash

# Check for firewall rules
iptables -L OUTPUT -n 2>/dev/null || echo "No iptables access"
ufw status 2>/dev/null || echo "No UFW"

# Check what the agent can reach
curl -s -o /dev/null -w "%{http_code}" https://httpbin.org/get --max-time 5

If the agent has unrestricted outbound access, flag as a security gap — a compromised agent could exfiltrate data to any destination.

Step 8: Produce Report#

Generate a structured report using the template in references/report-template.md. Include:

• Risk classification for each skill
• Specific findings with severity ratings
• Recommended remediations with priority
• Action tier assignments (see references/action-tiers.md)

Save report to memory/zt4ai-audit-YYYY-MM-DD.md.

Ongoing Monitoring#

After the initial audit:

1. Re-verify integrity after any skill install/update ( sha256sum -c against baseline)
2. Re-audit behavioral skills whenever they're updated — these are the highest risk
3. Update baseline after intentional skill modifications
4. Schedule periodic audits via cron (monthly recommended)

References#

• references/risk-classification.md — Detailed classification criteria with examples
• references/audit-checklist.md — Per-skill audit checklist
• references/action-tiers.md — Graduated trust model for agent actions
• references/report-template.md — Audit report template