Mmcp.market

agent-owasp-compliance skill

by github·github/awesome-copilot·39k stars·MIT

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"

A100/100content scan

Is the agent-owasp-compliance skill safe?

Clean: nothing in its files matched our rules. We read 1 file in the folder on 2026-09-28.

No findings.

Install the agent-owasp-compliance skill

A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.

git clone --depth 1 https://github.com/github/awesome-copilot.git /tmp/awesome-copilot
mkdir -p ~/.claude/skills
cp -r /tmp/awesome-copilot/skills/agent-owasp-compliance ~/.claude/skills/agent-owasp-compliance
available in every project

In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub

The instructions your agent would load

SKILL.md as published, without the frontmatter. Read it on GitHub

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"

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:

  • INTEGRITY.json or manifest files with SHA-256 hashes
  • Signature verification on plugin installation
  • Dependency pinning (no @latest, >= without upper bound)
  • SBOM generation

Check ASI-10: Behavioral Anomaly

Verify the system can detect and respond to agent behavioral drift.

What to search for:

  • Circuit breakers that trip on repeated failures
  • Trust score decay over time (temporal decay)
  • Kill switch or emergency stop capability
  • Anomaly detection on tool call patterns (frequency, targets, timing)

Failing: No mechanism to stop a misbehaving agent automatically. Passing: Circuit breaker trips after N failures, trust decays without activity, kill switch available.

More skills from github/awesome-copilot

  • Aacquire-codebase-knowledgeUse this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
  • Aacreadiness-assessRun the AgentRC readiness assessment on the current repository and produce a static HTML dashboard at reports/index.html. Wraps `npx github:microsoft/agentrc readiness` and hands off rendering to the @ai-readiness-reporter custom agent. Supports policies (--policy) for org-specific scoring. Use when asked to assess, audit, or score the AI readiness of a repo.
  • Aacreadiness-generate-instructionsGenerate tailored AI agent instruction files via AgentRC instructions command. Produces .github/copilot-instructions.md (default, recommended for Copilot in VS Code) plus optional per-area .instructions.md files with applyTo globs for monorepos. Use after running /acreadiness-assess to close gaps in the AI Tooling pillar.
  • Aacreadiness-policyHelp the user pick, write, or apply an AgentRC policy. Policies customise readiness scoring by disabling irrelevant checks, overriding impact/level, setting pass-rate thresholds, or chaining org baselines with team overrides. Use when the user asks about strict mode, AI-only scoring, custom weights, CI gating, or wants org-wide standardisation.
  • Aad-campaign-analyzerUse this skill when the user shares ad campaign performance data and asks what to cut, scale, or test. Trigger for prompts like "analyze my ad campaigns", "where am I wasting ad spend", "reallocate my ad budget", "which ads are actually working", or "ROAS analysis". Do not trigger for campaign planning or creative generation without performance data.
  • Aadd-educational-commentsAdd educational comments to the file specified, or prompt asking for file to comment if one is not provided.
  • Aadobe-illustrator-scriptingWrite, debug, and optimize Adobe Illustrator automation scripts using ExtendScript (JavaScript/JSX). Use when creating or modifying scripts that manipulate documents, layers, paths, text frames, colors, symbols, artboards, or any Illustrator DOM objects. Covers the complete JavaScript object model, coordinate system, measurement units, export workflows, and scripting best practices.
  • Aagent-architectureDesign AI agent architectures through requirements discovery, or audit and diagnose architectural flaws in existing agents. Architecture only; excludes implementation and general code review.
  • Aagent-governancePatterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
  • Aagent-skill-stackFind, evaluate, and assemble the smallest compatible set of AI Agent Skills for an end-to-end natural-language goal. Use when a user wants Skills for a multi-step workflow, asks which Skills fit a project, needs an installed-Skill audit or conflict check, has low Skill recall, wants indirect helpers such as humanizers or compliance checks, or wants a project-specific Skill Stack with controlled installation. Search local Skills, registries, GitHub, and OpenCLI; compare adoption, verified fit, safety, and overlap. Do not use for locating one known or common Skill; use the generic find-skills workflow.
  • Aagent-supply-chainVerify supply chain integrity for AI agent plugins, tools, and dependencies. Use this skill when: - Generating SHA-256 integrity manifests for agent plugins or tool packages - Verifying that installed plugins match their published manifests - Detecting tampered, modified, or untracked files in agent tool directories - Auditing dependency pinning and version policies for agent components - Building provenance chains for agent plugin promotion (dev → staging → production) - Any request like "verify plugin integrity", "generate manifest", "check supply chain", or "sign this plugin"
  • Aagentic-evalPatterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

All agent skills → · MCP servers