agent-owasp-compliance
Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"
What this skill does
# Agent OWASP ASI Compliance Check
Evaluate AI agent systems against the OWASP Agentic Security Initiative (ASI) Top 10 — the industry standard for agent security posture.
## Overview
The OWASP ASI Top 10 defines the critical security risks specific to autonomous AI agents — not LLMs, not chatbots, but agents that call tools, access systems, and act on behalf of users. This skill checks whether your agent implementation addresses each risk.
```
Codebase → Scan for each ASI control:
ASI-01: Prompt Injection Protection
ASI-02: Tool Use Governance
ASI-03: Agency Boundaries
ASI-04: Escalation Controls
ASI-05: Trust Boundary Enforcement
ASI-06: Logging & Audit
ASI-07: Identity Management
ASI-08: Policy Integrity
ASI-09: Supply Chain Verification
ASI-10: Behavioral Monitoring
→ Generate Compliance Report (X/10 covered)
```
## The 10 Risks
| Risk | Name | What to Look For |
|------|------|-----------------|
| ASI-01 | Prompt Injection | Input validation before tool calls, not just LLM output filtering |
| ASI-02 | Insecure Tool Use | Tool allowlists, argument validation, no raw shell execution |
| ASI-03 | Excessive Agency | Capability boundaries, scope limits, principle of least privilege |
| ASI-04 | Unauthorized Escalation | Privilege checks before sensitive operations, no self-promotion |
| ASI-05 | Trust Boundary Violation | Trust verification between agents, signed credentials, no blind trust |
| ASI-06 | Insufficient Logging | Structured audit trail for all tool calls, tamper-evident logs |
| ASI-07 | Insecure Identity | Cryptographic agent identity, not just string names |
| ASI-08 | Policy Bypass | Deterministic policy enforcement, no LLM-based permission checks |
| ASI-09 | Supply Chain Integrity | Signed plugins/tools, integrity verification, dependency auditing |
| ASI-10 | Behavioral Anomaly | Drift detection, circuit breakers, kill switch capability |
---
## Check ASI-01: Prompt Injection Protection
Look for input validation that runs **before** tool execution, not after LLM generation.
```python
import re
from pathlib import Path
def check_asi_01(project_path: str) -> dict:
"""ASI-01: Is user input validated before reaching tool execution?"""
positive_patterns = [
"input_validation", "validate_input", "sanitize",
"classify_intent", "prompt_injection", "threat_detect",
"PolicyEvaluator", "PolicyEngine", "check_content",
]
negative_patterns = [
r"eval\(", r"exec\(", r"subprocess\.run\(.*shell=True",
r"os\.system\(",
]
# Scan Python files for signals
root = Path(project_path)
positive_matches = []
negative_matches = []
for py_file in root.rglob("*.py"):
content = py_file.read_text(errors="ignore")
for pattern in positive_patterns:
if pattern in content:
positive_matches.append(f"{py_file.name}: {pattern}")
for pattern in negative_patterns:
if re.search(pattern, content):
negative_matches.append(f"{py_file.name}: {pattern}")
positive_found = len(positive_matches) > 0
negative_found = len(negative_matches) > 0
return {
"risk": "ASI-01",
"name": "Prompt Injection",
"status": "pass" if positive_found and not negative_found else "fail",
"controls_found": positive_matches,
"vulnerabilities": negative_matches,
"recommendation": "Add input validation before tool execution, not just output filtering"
}
```
**What passing looks like:**
```python
# GOOD: Validate before tool execution
result = policy_engine.evaluate(user_input)
if result.action == "deny":
return "Request blocked by policy"
tool_result = await execute_tool(validated_input)
```
**What failing looks like:**
```python
# BAD: User input goes directly to tool
tool_result = await execute_tool(user_input) # No validation
```
---
## Check ASI-02: Insecure Tool Use
Verify tools have allowlists, argument validation, and no unrestricted execution.
**What to search for:**
- Tool registration with explicit allowlists (not open-ended)
- Argument validation before tool execution
- No `subprocess.run(shell=True)` with user-controlled input
- No `eval()` or `exec()` on agent-generated code without sandbox
**Passing example:**
```python
ALLOWED_TOOLS = {"search", "read_file", "create_ticket"}
def execute_tool(name: str, args: dict):
if name not in ALLOWED_TOOLS:
raise PermissionError(f"Tool '{name}' not in allowlist")
# validate args...
return tools[name](**validated_args)
```
---
## Check ASI-03: Excessive Agency
Verify agent capabilities are bounded — not open-ended.
**What to search for:**
- Explicit capability lists or execution rings
- Scope limits on what the agent can access
- Principle of least privilege applied to tool access
**Failing:** Agent has access to all tools by default.
**Passing:** Agent capabilities defined as a fixed allowlist, unknown tools denied.
---
## Check ASI-04: Unauthorized Escalation
Verify agents cannot promote their own privileges.
**What to search for:**
- Privilege level checks before sensitive operations
- No self-promotion patterns (agent changing its own trust score or role)
- Escalation requires external attestation (human or SRE witness)
**Failing:** Agent can modify its own configuration or permissions.
**Passing:** Privilege changes require out-of-band approval (e.g., Ring 0 requires SRE attestation).
---
## Check ASI-05: Trust Boundary Violation
In multi-agent systems, verify that agents verify each other's identity before accepting instructions.
**What to search for:**
- Agent identity verification (DIDs, signed tokens, API keys)
- Trust score checks before accepting delegated tasks
- No blind trust of inter-agent messages
- Delegation narrowing (child scope <= parent scope)
**Passing example:**
```python
def accept_task(sender_id: str, task: dict):
trust = trust_registry.get_trust(sender_id)
if not trust.meets_threshold(0.7):
raise PermissionError(f"Agent {sender_id} trust too low: {trust.current()}")
if not verify_signature(task, sender_id):
raise SecurityError("Task signature verification failed")
return process_task(task)
```
---
## Check ASI-06: Insufficient Logging
Verify all agent actions produce structured, tamper-evident audit entries.
**What to search for:**
- Structured logging for every tool call (not just print statements)
- Audit entries include: timestamp, agent ID, tool name, args, result, policy decision
- Append-only or hash-chained log format
- Logs stored separately from agent-writable directories
**Failing:** Agent actions logged via `print()` or not logged at all.
**Passing:** Structured JSONL audit trail with chain hashes, exported to secure storage.
---
## Check ASI-07: Insecure Identity
Verify agents have cryptographic identity, not just string names.
**Failing indicators:**
- Agent identified by `agent_name = "my-agent"` (string only)
- No authentication between agents
- Shared credentials across agents
**Passing indicators:**
- DID-based identity (`did:web:`, `did:key:`)
- Ed25519 or similar cryptographic signing
- Per-agent credentials with rotation
- Identity bound to specific capabilities
---
## Check ASI-08: Policy Bypass
Verify policy enforcement is deterministic — not LLM-based.
**What to search for:**
- Policy evaluation uses deterministic logic (YAML rules, code predicates)
- No LLM calls in the enforcement path
- Policy checks cannot be skipped or overridden by the agent
- Fail-closed behavior (if policy check errors, action is denied)
**Failing:** Agent decides its own permissions via prompt ("Am I allowed to...?").
**Passing:** PolicyEvaluator.evaluate() returns allow/deny in <0.1ms, no LLM involved.
---
## Check ASI-09: Supply Chain Integrity
Verify agent plugins and tools have integrity verification.
**What to search for:**
- `INTEGRITY.json` or manifest files with SHA-2Related in AI Agents
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