Agent Owasp Compliance

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

Check ASI-01: Prompt Injection Protection

Look for input validation that runs before tool execution, not after LLM generation.

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:

# 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:

# 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:

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:

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: