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Agent Owasp Compliance

OrganizationPopular
github
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"

Overview

Publishergithub
Repositoryawesome-copilot
Skill nameagent-owasp-compliance
Stars
39.1K
Forks
5K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by github on GitHub. Read the source before you install it.

Installation

Install the Agent Owasp Compliance AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
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
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Owasp Compliance in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Agent Owasp Compliance on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Agent Owasp Compliance is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

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-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

markdown
# 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

Frequently asked questions

What does the Agent Owasp Compliance AI skill do?

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"

Why use Agent Owasp Compliance on TypingMind?

Because you install it once and use it with any model. Agent Owasp Compliance is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Agent Owasp Compliance in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/github/awesome-copilot/tree/main/skills/agent-owasp-compliance. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Agent Owasp Compliance?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Agent Owasp Compliance?

As many as you like. As long as a model supports skills, you can use Agent Owasp Compliance with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Agent Owasp Compliance AI skill free?

Yes. It is published on GitHub by github under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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