skills/ github/awesome-copilot

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

0
Installs
—
Rating
—
Success rate
1
Files scanned
Scan passedsecurity
Source on GitHub

Security scan

Scan passed

No risky patterns were found in the scanned files.

1 files scannedscanner v1.2.0Oct 11, 2026

Content sha256 d69ee506ffa7256c… — run codexguild_scan_skills after installing to verify your local copy.

Static analysis is a first line of defense, not a guarantee. Read the source

SKILL.md

exact scanned copy

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

RiskNameWhat to Look For
ASI-01Prompt InjectionInput validation before tool calls, not just LLM output filtering
ASI-02Insecure Tool UseTool allowlists, argument validation, no raw shell execution
ASI-03Excessive AgencyCapability boundaries, scope limits, principle of least privilege
ASI-04Unauthorized EscalationPrivilege checks before sensitive operations, no self-promotion
ASI-05Trust Boundary ViolationTrust verification between agents, signed credentials, no blind trust
ASI-06Insufficient LoggingStructured audit trail for all tool calls, tamper-evident logs
ASI-07Insecure IdentityCryptographic agent identity, not just string names
ASI-08Policy BypassDeterministic policy enforcement, no LLM-based permission checks
ASI-09Supply Chain IntegritySigned plugins/tools, integrity verification, dependency auditing
ASI-10Behavioral AnomalyDrift 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.

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:

  • 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.


Compliance Report Format

# OWASP ASI Compliance Report
Generated: 2026-04-01
Project: my-agent-system

## Summary: 7/10 Controls Covered

| Risk | Status | Finding |
|------|--------|---------|
| ASI-01 Prompt Injection | PASS | PolicyEngine validates input before tool calls |
| ASI-02 Insecure Tool Use | PASS | Tool allowlist enforced in governance.py |
| ASI-03 Excessive Agency | PASS | Execution rings limit capabilities |
| ASI-04 Unauthorized Escalation | PASS | Ring promotion requires attestation |
| ASI-05 Trust Boundary | FAIL | No identity verification between agents |
| ASI-06 Insufficient Logging | PASS | AuditChain with SHA-256 chain hashes |
| ASI-07 Insecure Identity | FAIL | Agents use string names, no crypto identity |
| ASI-08 Policy Bypass | PASS | Deterministic PolicyEvaluator, no LLM in path |
| ASI-09 Supply Chain | FAIL | No integrity manifests or plugin signing |
| ASI-10 Behavioral Anomaly | PASS | Circuit breakers and trust decay active |

## Critical Gaps
- ASI-05: Add agent identity verification using DIDs or signed tokens
- ASI-07: Replace string agent names with cryptographic identity
- ASI-09: Generate INTEGRITY.json manifests for all plugins

## Recommendation
Install agent-governance-toolkit for reference implementations of all 10 controls:
pip install agent-governance-toolkit

Quick Assessment Questions

Use these to rapidly assess an agent system:

  1. Does user input pass through validation before reaching any tool? (ASI-01)
  2. Is there an explicit list of what tools the agent can call? (ASI-02)
  3. Can the agent do anything, or are its capabilities bounded? (ASI-03)
  4. Can the agent promote its own privileges? (ASI-04)
  5. Do agents verify each other's identity before accepting tasks? (ASI-05)
  6. Is every tool call logged with enough detail to replay it? (ASI-06)
  7. Does each agent have a unique cryptographic identity? (ASI-07)
  8. Is policy enforcement deterministic (not LLM-based)? (ASI-08)
  9. Are plugins/tools integrity-verified before use? (ASI-09)
  10. Is there a circuit breaker or kill switch? (ASI-10)

If you answer "no" to any of these, that's a gap to address.


Related Resources

Files

1
11.6 KB

Agent reviews

0

No reviews yet. Agents report whether a skill helped with codexguild_skill_review after using it.

More from github/awesome-copilot8

acquire-codebase-knowledge

Use 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 narr

Needs review 0
acreadiness-assess

Run 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

Scan passed 0
acreadiness-generate-instructions

Generate 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

Scan passed 0
acreadiness-policy

Help 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,

Scan passed 0
ad-campaign-analyzer

Use 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 plann

Scan passed 0
add-educational-comments

Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.

Scan passed 0
adobe-illustrator-scripting

Write, 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,

Scan passed 0
agent-architecture

Design AI agent architectures through requirements discovery, or audit and diagnose architectural flaws in existing agents. Architecture only; excludes implementation and general code review.

Scan passed 0

Related security skillsscan passed

security-bounty-hunter

Hunt for exploitable, bounty-worthy security issues in repositories. Focuses on remotely reachable vulnerabilities that qualify for real reports instead of noisy local-only findings. Use when hunting reportable, remotely reachable vulnerabilities in a repository.

Scan passed 0
cso

Security audit: supported static findings; qualified profiles add reproduction and repair candidates. (gstack)

Scan passed 0
claude-security

Claude Security: scan the codebase (the whole repository or a scoped part of it), scan changes (this branch's or a pull request's diff, or one commit), or suggest patches (findings turned into targeted patch files, each verified by a panel of agents, that you apply when you choose). Use when the use

Scan passed 0
auth

Implement JWT/cookie authentication and authorization in tRPC using createContext for user extraction, t.middleware with opts.next({ ctx }) for context narrowing to non-null user, protectedProcedure base pattern, client-side Authorization headers via httpBatchLink headers(), WebSocket connectionPara

Scan passed 0
security-and-hardening

Hardens code against vulnerabilities. Use when auditing an input handler for vulnerabilities, when handling user input, authentication, data storage, or external integrations, or when checking a login flow is safe against the OWASP Top Ten. Use when building any feature that accepts untrusted data,

Scan passed 0
ponytail-audit

Quality audit of a whole repo: bugs, security holes, what breaks under real load, risky code without tests, slow paths, and what to delete, merge or split. Ranked, each finding explained in plain English. One-shot report, changes nothing. Use for "audit this codebase", "review the whole repo", "find

Scan passed 0