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docs: OWASP Agentic Top 10 reference architecture mapping#843
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Description

Adds a comprehensive reference architecture document mapping each OWASP Agentic Top 10 (2026) risk (ASI01-ASI10) to concrete AGT implementation patterns with file:line code citations and Mermaid architecture diagrams.

This follows the same format and rigor as the existing docs/compliance/owasp-llm-top10-mapping.md but adapted for the 2026 Agentic Security Initiative taxonomy.

Key sections

  • Executive Summary — coverage table showing 10/10 Partial (0 Full, 0 Gap)
  • Methodology — explicit Full/Partial/Gap criteria with code-first evidence standard
  • Per-Risk Reference Architecture (ASI01-ASI10) — each with risk description, Mermaid diagram, AGT component citations, honest coverage assessment, and implementation evidence
  • Cross-Cutting Patterns — 5 shared architectural principles (tamper-evident audit, policy-first enforcement, trust-gated delegation, integrity over names, containment not just detection)
  • Gap Analysis — 6 concrete gaps with evidence and recommendations

Honesty note

Every risk is assessed as Partial — strong standalone controls exist but are not universally auto-wired into every execution path. The document does not overclaim Full coverage.

Type of Change

  • Documentation update

Package(s) Affected

  • docs / root

Checklist

  • My code follows the project style guidelines (ruff check)
  • I have added tests that prove my fix/feature works
  • All new and existing tests pass (pytest)
  • I have updated documentation as needed
  • I have signed the Microsoft CLA

Related Issues

Relates to Discussion #814 (Agentic Standards Landscape - OWASP reference architectures)

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🤖 AI Agent: code-reviewer

Review Feedback for Pull Request: OWASP Agentic Top 10 Reference Architecture Mapping

This PR introduces a comprehensive reference architecture document mapping OWASP Agentic Top 10 risks to the AGT implementation. The document is thorough, code-first, and honest in its assessment of coverage gaps. Below is the review feedback categorized by focus areas:


🔴 CRITICAL: Security Issues

  1. ASI03: Identity & Privilege Abuse

    • Issue: Delegation validation (verify_delegation) does not cryptographically bind trust metadata end-to-end. The A2A envelope stores trust metadata as fields rather than using a signed/authenticated message envelope (packages\agentmesh-integrations\a2a-protocol\a2a_agentmesh\task.py:196-205).
    • Impact: This creates a potential attack vector where trust metadata could be tampered with, leading to privilege escalation or impersonation attacks.
    • Action: Implement cryptographic signing of A2A task envelopes and enforce signature validation at all points of trust metadata consumption.
  2. ASI07: Insecure Inter-Agent Communication

    • Issue: Integrity and trust checks exist, but message confidentiality and signed envelope transport are not enforced in the A2A adapter (packages\agentmesh-integrations\a2a-protocol\a2a_agentmesh\task.py:184-205).
    • Impact: Without end-to-end encryption and signed transport, inter-agent communication is vulnerable to interception and tampering.
    • Action: Introduce mandatory encryption (e.g., TLS) and signed envelopes for all inter-agent communication.
  3. ASI05: Unexpected Code Execution (RCE)

    • Issue: The sandbox rules in packages\agent-os\src\agent_os\sandbox.py are explicitly labeled as "sample starting points" and lack comprehensive hardening.
    • Impact: This leaves room for sandbox escape and arbitrary code execution, especially in production environments.
    • Action: Harden the sandbox implementation by enforcing stricter rules, such as disabling dynamic imports, restricting file system access, and integrating runtime monitoring for suspicious behavior.

🟡 WARNING: Potential Breaking Changes

  1. Universal Auto-Wiring of Controls

    • Issue: Many controls (e.g., MemoryGuard, PromptInjectionDetector, PolicyInterceptor) are standalone and not universally auto-wired into all execution paths (packages\agent-os\src\agent_os\integrations\base.py:927-975).
    • Impact: Retrofitting these controls into all adapters might break existing integrations or workflows.
    • Action: Introduce a backward-compatible mechanism (e.g., feature flags or adapter-specific configuration) to gradually enforce universal auto-wiring without disrupting existing users.
  2. End-to-End Signed Inter-Agent Messages

    • Issue: Adding cryptographic signing to A2A envelopes may require changes to existing APIs and workflows (packages\agentmesh-integrations\a2a-protocol\a2a_agentmesh\task.py:196-205).
    • Impact: This could break compatibility with older versions of the protocol or existing integrations.
    • Action: Provide a migration path and maintain backward compatibility for legacy systems while introducing signed envelopes.

💡 Suggestions for Improvement

  1. Gap Analysis Recommendations

    • Suggestion: Include actionable recommendations for addressing the identified gaps in the "Gap Analysis" section. For example, propose specific implementation strategies for universal auto-wiring or cryptographic enhancements.
  2. Cross-Package Integration

    • Suggestion: Consider creating a unified governance layer that automatically integrates key controls (e.g., MemoryGuard, PolicyInterceptor, PromptInjectionDetector) across all packages. This would reduce the risk of inconsistent enforcement.
  3. Testing Coverage

    • Suggestion: Add test cases to validate the effectiveness of the controls mentioned in the document. For example:
      • Test the sandbox against known escape vectors.
      • Verify the integrity of signed A2A envelopes.
      • Ensure MemoryGuard blocks all known poisoning patterns.
  4. Documentation Style

    • Suggestion: While the document is thorough, consider breaking it into smaller, modular sections for easier navigation. For example:
      • Separate the "Cross-Cutting Patterns" into its own document.
      • Provide a summary table for implementation gaps and recommendations.
  5. Backward Compatibility

    • Suggestion: For each gap identified, explicitly outline the impact on backward compatibility and propose strategies to mitigate disruptions for existing users.

Summary

This PR is a significant step forward in documenting AGT's security architecture and aligning it with the OWASP Agentic Top 10. However, several critical security issues need to be addressed, particularly around cryptographic operations and sandbox hardening. Additionally, care must be taken to ensure backward compatibility when addressing gaps like universal auto-wiring and signed inter-agent messages.

Action Items:

  1. Address 🔴 CRITICAL issues with cryptographic signing and sandbox hardening.
  2. Plan for 🟡 WARNING changes with backward compatibility in mind.
  3. Implement 💡 Suggestions to improve documentation structure, testing coverage, and gap analysis recommendations.

Let me know if you need further clarification or assistance!

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🤖 AI Agent: security-scanner — Security Review of Pull Request: OWASP Agentic Top 10 Reference Architecture Mapping

Security Review of Pull Request: OWASP Agentic Top 10 Reference Architecture Mapping

This pull request primarily introduces documentation updates, specifically mapping the OWASP Agentic Top 10 (ASI01–ASI10) risks to implementation patterns in the Agent Governance Toolkit (AGT). While the changes are documentation-focused, the referenced code and architecture patterns are critical to understanding the security posture of AGT. Below is the security analysis based on the provided diff and description.


Findings

1. Prompt Injection Defense Bypass (ASI01)

Rating: 🔴 CRITICAL
Attack Vector:
The documentation highlights that the PromptInjectionDetector is not universally invoked across all execution paths. Specifically, the BaseIntegration.pre_execute() lifecycle does not automatically enforce prompt injection detection (packages\agent-os\src\agent_os\integrations\base.py:927-975). This creates a bypass vector where crafted input could circumvent the detector, especially in non-MCP-specific paths.

Suggested Fix:

  • Refactor BaseIntegration.pre_execute() to invoke PromptInjectionDetector by default for all adapters.
  • Ensure fail-closed behavior for any detection failures.
  • Add unit tests to verify prompt injection detection is enforced across all execution paths.

2. Policy Engine Circumvention (ASI02)

Rating: 🟠 HIGH
Attack Vector:
Tool governance controls are distributed across multiple packages (mcp-proxy, mcp-trust-proxy, agent-os), but there is no single, default enforcement pipeline for tool governance. This fragmentation could allow attackers to exploit adapters that do not integrate these controls (packages\agent-mesh\packages\mcp-proxy\src\proxy.ts:147-206).

Suggested Fix:

  • Consolidate tool governance into a unified enforcement pipeline that all adapters must use.
  • Enforce policy checks at the adapter level by default.
  • Perform integration testing to ensure all adapters comply with governance rules.

3. Trust Chain Weaknesses (ASI03)

Rating: 🔴 CRITICAL
Attack Vector:
Delegation validation (verify_delegation()) is trust-threshold based but does not cryptographically bind trust metadata end-to-end. Similarly, A2A task envelopes store trust metadata as fields rather than signed/authenticated message envelopes (packages\agentmesh-integrations\a2a-protocol\a2a_agentmesh\task.py:196-205). This opens the door to privilege abuse or impersonation attacks.

Suggested Fix:

  • Implement cryptographic binding for delegation validation using signed artifacts.
  • Enforce signed and authenticated A2A task envelopes.
  • Add cryptographic integrity checks for trust metadata.

4. Credential Exposure

Rating: 🔵 LOW
Attack Vector:
No direct evidence of credential exposure was found in the provided diff. However, the documentation mentions tamper-evident audit logs and approval gating (packages\agent-mesh\packages\mcp-proxy\src\audit.ts:27-123), which should be reviewed for potential logging of sensitive data.

Suggested Fix:

  • Audit logging mechanisms to ensure sensitive data (e.g., credentials, tokens) are redacted.
  • Add automated tests to verify sensitive data is not exposed in logs.

5. Sandbox Escape (ASI05)

Rating: 🟠 HIGH
Attack Vector:
The sandboxing implementation (packages\agent-os\src\agent_os\sandbox.py) explicitly labels itself as a sample starting point, not a hardened containment boundary. This leaves room for sandbox escape via dynamic imports, AST manipulation, or unsafe execution paths.

Suggested Fix:

  • Harden sandbox rules to block dynamic imports and runtime code execution.
  • Use containerized execution environments (e.g., Docker, Firecracker) for stronger isolation.
  • Perform penetration testing to validate sandbox containment.

6. Deserialization Attacks

Rating: 🟡 MEDIUM
Attack Vector:
The sandbox implementation (packages\agent-os\src\agent_os\sandbox.py) uses yaml.safe_load() for loading rules, which is safer than yaml.load(). However, deserialization attacks could still occur if malicious YAML files are loaded.

Suggested Fix:

  • Validate YAML input before deserialization.
  • Use stricter schema validation for sandbox rules.
  • Add tests to detect deserialization vulnerabilities.

7. Race Conditions (ASI04)

Rating: 🟠 HIGH
Attack Vector:
TOCTOU vulnerabilities are mentioned in the supply chain controls (packages\agent-mesh\src\agentmesh\marketplace\installer.py:119-123). Re-verification after dependency resolution reduces risk but does not eliminate it entirely. An attacker could exploit timing gaps to inject malicious dependencies.

Suggested Fix:

  • Implement atomic operations for dependency resolution and verification.
  • Use secure package managers that support integrity checks (e.g., TUF).
  • Perform stress testing to identify race conditions in dependency handling.

8. Supply Chain Vulnerabilities (ASI04)

Rating: 🔴 CRITICAL
Attack Vector:
The supply chain controls are strong for plugins and MCP tools but do not extend uniformly across all models, dependencies, and runtime bundles (packages\agent-mesh\src\agentmesh\marketplace\signing.py:22-85). This creates gaps where compromised components could enter the stack.

Suggested Fix:

  • Extend signature verification and SBOM validation to all components in the stack.
  • Require signed artifacts for all dependencies, models, and runtime bundles.
  • Integrate OpenSSF Scorecard or similar tools for dependency risk assessment.

Summary of Findings

Category Rating Fix Priority
Prompt Injection Defense 🔴 CRITICAL Immediate
Policy Engine Circumvention 🟠 HIGH High
Trust Chain Weaknesses 🔴 CRITICAL Immediate
Credential Exposure 🔵 LOW Low
Sandbox Escape 🟠 HIGH High
Deserialization Attacks 🟡 MEDIUM Medium
Race Conditions 🟠 HIGH High
Supply Chain Vulnerabilities 🔴 CRITICAL Immediate

General Recommendations

  1. Automated Testing: Expand test coverage to include security-specific tests for prompt injection, sandbox escape, deserialization, and supply chain vulnerabilities.
  2. Code Review: Conduct manual reviews of critical paths (e.g., BaseIntegration, sandbox.py, mcp-proxy) to identify potential bypasses or weaknesses.
  3. Dependency Management: Audit third-party dependencies for typosquatting and outdated libraries.
  4. Cryptographic Hardening: Enforce signed artifacts and cryptographic integrity checks across all trust and supply chain paths.
  5. Documentation Accuracy: Ensure the reference architecture document accurately reflects the current state of controls and does not overstate coverage.

This pull request provides valuable documentation, but the referenced gaps in implementation highlight areas requiring immediate attention to strengthen AGT's security posture.

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🤖 AI Agent: code-reviewer

Review Summary

This pull request introduces a comprehensive reference architecture document mapping the OWASP Agentic Top 10 (ASI01–ASI10) risks to the implementation patterns in the Agent Governance Toolkit (AGT). The document is thorough, well-structured, and adheres to a code-first evidence standard, which is critical for security-focused documentation. The honesty in coverage assessment (marking all risks as "Partial") is commendable and aligns with best practices for security transparency.

However, the document highlights several architectural gaps and limitations in the AGT implementation, particularly around universal auto-wiring, cryptographic binding, and end-to-end enforcement. These gaps represent potential security risks and areas for improvement in the AGT codebase.


🔴 CRITICAL Issues

  1. Cryptographic Binding for A2A Trust Metadata (ASI03)
    The A2A envelope stores trust metadata as fields rather than using a signed/authenticated message envelope. This creates a risk of tampering or impersonation in inter-agent communication.
    Actionable Recommendation:

    • Implement cryptographic signing for A2A task envelopes to ensure integrity and authenticity. Use Ed25519 or similar algorithms for lightweight and secure signing.
    • Update packages\agentmesh-integrations\a2a-protocol\a2a_agentmesh\task.py to enforce signed envelopes for all inter-agent communication.
  2. Insecure Inter-Agent Communication (ASI07)
    While handshake signing and trust gating exist, message confidentiality and signed envelope transport are not enforced in the A2A adapter. This leaves inter-agent communication vulnerable to interception or tampering.
    Actionable Recommendation:

    • Introduce end-to-end encryption for inter-agent communication using protocols like TLS or SPIFFE/SVID.
    • Ensure that all A2A messages are signed and encrypted by default in packages\agentmesh-integrations\a2a-protocol\a2a_agentmesh\task.py.
  3. Sandbox Escape Vectors (ASI05)
    The current sandbox implementation in packages\agent-os\src\agent_os\sandbox.py explicitly labels itself as a sample starting point, which is insufficient for production-grade security. This creates a risk of remote code execution (RCE) if the sandbox is not hardened.
    Actionable Recommendation:

    • Harden the sandbox implementation by enforcing stricter rules for imports, system calls, and resource access.
    • Consider integrating a third-party sandboxing library or containerization for stronger isolation.

🟡 WARNING: Potential Breaking Changes

  1. Universal Auto-Wiring of Security Controls
    Many security controls (e.g., MemoryGuard, PromptInjectionDetector, PolicyInterceptor) are not universally wired into all execution paths. While this allows flexibility, it creates a risk of inconsistent enforcement across adapters.
    Actionable Recommendation:

    • Refactor BaseIntegration in packages\agent-os\src\agent_os\integrations\base.py to automatically invoke critical security controls (e.g., MemoryGuard, PromptInjectionDetector) for all execution paths.
    • This change may break existing integrations that rely on manual invocation of these controls. Provide clear migration guidance and deprecation warnings.
  2. End-to-End Supply Chain Verification (ASI04)
    Supply chain controls are strong for plugins and MCP tools but do not extend uniformly to all models, dependencies, and runtime bundles. Expanding these controls may require changes to existing installation and execution workflows.
    Actionable Recommendation:

    • Introduce a uniform SBOM (Software Bill of Materials) and signature verification pipeline for all dependencies and runtime artifacts.
    • Update packages\agent-mesh\src\agentmesh\marketplace\installer.py and packages\agent-os\src\agent_os\integrations\base.py to enforce these checks.

💡 Suggestions for Improvement

  1. Automated Testing for Security Controls
    The document mentions strong standalone controls but does not indicate whether these are covered by automated tests.
    Actionable Recommendation:

    • Add unit and integration tests to verify the behavior of security controls like MemoryGuard, PromptInjectionDetector, and PolicyInterceptor.
    • Use pytest fixtures to simulate attack scenarios and validate that controls behave as expected.
  2. OWASP Compliance Automation
    The OWASP Agentic Top 10 mapping is manual and static. Automating compliance checks would improve maintainability and reduce the risk of drift.
    Actionable Recommendation:

    • Develop a CI/CD workflow to automatically validate AGT's compliance with OWASP Agentic Top 10 risks.
    • Use tools like Bandit, Semgrep, or custom scripts to enforce compliance rules.
  3. Documentation Enhancements
    While the document is thorough, it could benefit from additional clarity and examples for developers unfamiliar with OWASP standards.
    Actionable Recommendation:

    • Add code snippets or pseudocode for each mitigation pattern to make the document more actionable for developers.
    • Include links to relevant OWASP resources for deeper context.

Final Assessment

This pull request is a valuable addition to the AGT documentation, providing a transparent and detailed mapping of OWASP Agentic Top 10 risks. However, the identified gaps in cryptographic binding, inter-agent communication security, and sandbox hardening are critical issues that must be addressed to ensure robust security. Additionally, the potential breaking changes related to auto-wiring and supply chain verification require careful planning and migration support.

Approval Status:

  • Documentation: ✅ Approved
  • Security: 🔴 Requires critical fixes
  • Backward Compatibility: 🟡 Requires migration guidance

Please address the critical issues and warnings before merging.

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Content is excellent. spell-check CI is failing — please add the flagged terms to .cspell-repo-terms.txt or fix the typos, then this is ready.

@jackbatzner jackbatzner force-pushed the jb/owasp-agentic-reference-architecture branch from 3a471e3 to b5d4696 Compare April 6, 2026 20:12
@github-actions github-actions bot added the size/XL Extra large PR (500+ lines) label Apr 6, 2026
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🤖 AI Agent: code-reviewer

Review Feedback for Pull Request: OWASP Agentic Top 10 Reference Architecture Mapping


🔴 CRITICAL: Cryptographic Integrity for A2A Communication

The reference architecture highlights that the A2A communication protocol (packages/agentmesh-integrations/a2a-protocol/a2a_agentmesh/task.py) does not enforce cryptographic binding of trust metadata to the task envelope. This creates a potential attack vector where metadata could be tampered with or spoofed, leading to privilege escalation or unauthorized delegation.

Actionable Recommendation:

  1. Implement cryptographic signing of the entire A2A task envelope, including trust metadata, using Ed25519 or another secure algorithm.
  2. Validate the signature at the receiving end before processing the task.

🔴 CRITICAL: Lack of Universal Auto-Wiring for Security Controls

The document notes that several critical security controls (e.g., PromptInjectionDetector, MemoryGuard, PolicyInterceptor) are not universally auto-wired into all execution paths. This creates bypass opportunities for adapters or integrations that do not explicitly invoke these controls.

Actionable Recommendation:

  1. Refactor the BaseIntegration lifecycle (packages/agent-os/src/agent_os/integrations/base.py) to enforce mandatory invocation of key security controls (e.g., prompt injection detection, memory validation, policy enforcement) for all adapters.
  2. Add unit tests to verify that these controls are invoked in every execution path.

🔴 CRITICAL: Insecure Inter-Agent Communication

The reference architecture highlights that inter-agent communication (packages/agentmesh-integrations/a2a-protocol/a2a_agentmesh/task.py) lacks mandatory message confidentiality and signed envelope transport. This exposes agents to eavesdropping, tampering, and replay attacks.

Actionable Recommendation:

  1. Implement end-to-end encryption for inter-agent communication using TLS or similar protocols.
  2. Ensure that all inter-agent messages are signed and verified to prevent tampering and replay attacks.

💡 SUGGESTION: Harden Sandboxing Mechanisms

The current sandbox implementation (packages/agent-os/src/agent_os/sandbox.py) is labeled as a "sample starting point" and lacks comprehensive isolation. While it provides basic protections, it is not sufficient for high-assurance environments.

Actionable Recommendation:

  1. Extend the sandbox to include runtime monitoring for system calls, memory access, and network activity.
  2. Consider integrating with containerization technologies (e.g., Docker, Firecracker) for stronger isolation.

💡 SUGGESTION: Improve Supply Chain Security Coverage

The supply chain security controls (e.g., packages/agent-mesh/src/agentmesh/marketplace/installer.py) are robust for plugins and tools but do not extend to all dependencies, models, and runtime bundles.

Actionable Recommendation:

  1. Implement a unified SBOM (Software Bill of Materials) generation and verification pipeline for all components in the agent stack.
  2. Enforce signature verification for all dependencies, including Python packages and external models.

💡 SUGGESTION: Enhance Human-Agent Trust Controls

The current human-agent trust controls (e.g., tamper-evident audit logs, approval gating) are limited to approval and confidence thresholds. They do not include provenance tracking or fact-verification mechanisms.

Actionable Recommendation:

  1. Introduce a provenance tracking system that records the origin and transformation history of agent-generated outputs.
  2. Implement fact-verification stages for critical outputs to ensure alignment with human expectations.

🟡 WARNING: Backward Compatibility Risks

The proposed changes to the documentation introduce new terminology and mappings (e.g., ASI01–ASI10 risks). If these terms are adopted in the codebase, they may require updates to existing APIs, configurations, and documentation, potentially breaking backward compatibility.

Actionable Recommendation:

  1. Ensure that any future code changes related to ASI01–ASI10 mappings maintain backward compatibility with existing ATxx identifiers.
  2. Provide a migration guide for users transitioning from ATxx to ASIxx.

💡 SUGGESTION: Improve Type Safety and Validation

The reference architecture mentions several areas where typed models (e.g., plugin manifests, task envelopes) are used. However, it does not explicitly discuss the use of Pydantic or similar libraries for runtime validation.

Actionable Recommendation:

  1. Use Pydantic models for all typed data structures (e.g., plugin manifests, task envelopes) to enforce schema validation at runtime.
  2. Add unit tests to verify that invalid data is rejected.

💡 SUGGESTION: Expand OWASP Agentic Top 10 Coverage

The document honestly assesses all risks as "Partial" and identifies gaps in universal auto-wiring, cryptographic binding, and end-to-end enforcement. While this is a good start, the project could aim for "Full" coverage in future iterations.

Actionable Recommendation:

  1. Prioritize closing the identified gaps (e.g., universal auto-wiring, cryptographic binding) in the next development cycle.
  2. Update the reference architecture to reflect progress toward "Full" coverage.

Summary of Feedback

  • 🔴 CRITICAL: Address cryptographic integrity, universal auto-wiring, and inter-agent communication vulnerabilities.
  • 💡 SUGGESTION: Harden sandboxing, supply chain security, and human-agent trust controls.
  • 🟡 WARNING: Monitor backward compatibility risks related to terminology changes.
  • 💡 SUGGESTION: Use Pydantic for type safety and validation.

This pull request provides a valuable reference architecture for OWASP Agentic Top 10 compliance but highlights critical areas for improvement in the underlying implementation. Addressing these issues will significantly enhance the security posture of the Agent Governance Toolkit.

jackbatzner and others added 3 commits April 8, 2026 08:30
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
…eference architecture

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
@jackbatzner jackbatzner force-pushed the jb/owasp-agentic-reference-architecture branch from b5d4696 to 4f0fe85 Compare April 8, 2026 13:30
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🤖 AI Agent: code-reviewer

Feedback on Pull Request: OWASP Agentic Top 10 Reference Architecture Mapping


🔴 CRITICAL: Security Concerns

  1. Incomplete Cryptographic Binding in A2A Communication (ASI03, ASI07)
    The A2A task envelope (packages\agentmesh-integrations\a2a-protocol\a2a_agentmesh\task.py) stores trust metadata as fields rather than using a cryptographically signed/authenticated message envelope. This leaves inter-agent communication vulnerable to tampering or replay attacks.
    Actionable Recommendation:

    • Implement end-to-end cryptographic binding for A2A task envelopes using SPIFFE/SVID or similar standards.
    • Ensure that trust metadata and delegation artifacts are signed and verified at both ends of the communication pipeline.
  2. Sandboxing Limitations (ASI05)
    The Python sandbox (packages\agent-os\src\agent_os\sandbox.py) explicitly warns that its rules are sample starting points rather than a complete isolation boundary. This could allow sandbox escape or unsafe code execution in production environments.
    Actionable Recommendation:

    • Harden the sandbox implementation by integrating stricter runtime isolation mechanisms, such as containerization (e.g., Docker) or VM-based execution.
    • Expand blocked imports and builtins to cover additional attack vectors, such as dynamic code execution (eval, exec) and filesystem manipulation.
  3. Lack of Universal Auto-Wiring for Security Controls (Multiple Risks)
    Many security controls (e.g., PromptInjectionDetector, MemoryGuard, PolicyInterceptor) are not universally auto-wired into all execution paths. This creates bypass opportunities for adapters that do not explicitly invoke these controls.
    Actionable Recommendation:

    • Refactor the BaseIntegration lifecycle (packages\agent-os\src\agent_os\integrations\base.py) to enforce mandatory invocation of critical security controls across all adapters.
    • Introduce a centralized security pipeline that adapters must inherit or invoke.

🟡 WARNING: Potential Breaking Changes

  1. Backward Compatibility of Security Enhancements
    Strengthening cryptographic bindings (e.g., signed A2A envelopes) or sandboxing mechanisms may require changes to existing APIs or runtime configurations. This could break backward compatibility for users relying on current behavior.
    Actionable Recommendation:

    • Provide clear migration paths and versioning for any breaking changes.
    • Use feature flags or configuration options to allow users to opt into stricter security measures incrementally.
  2. Uniform Tool Governance Pipeline
    Consolidating tool governance across MCP proxy, trust proxy, and Agent OS (packages\agent-mesh\packages\mcp-proxy\src\proxy.ts, packages\agentmesh-integrations\mcp-trust-proxy\mcp_trust_proxy\proxy.py, packages\agent-os\src\agent_os\integrations\base.py) may require significant refactoring. This could disrupt existing integrations.
    Actionable Recommendation:

    • Deprecate fragmented governance paths gradually while introducing the unified pipeline.
    • Maintain backward compatibility by supporting legacy paths during the transition.

💡 Suggestions for Improvement

  1. OWASP Compliance Documentation
    The reference architecture document is thorough and well-structured. However, consider adding a "Future Work" section to explicitly outline planned improvements for achieving full coverage of OWASP Agentic Top 10 risks. This will help align contributors and stakeholders on roadmap priorities.

  2. Cross-Package Security Composition
    The current implementation demonstrates strong security patterns within individual packages but lacks cross-package composition. For example, MemoryGuard is not universally applied across RAG store paths.
    Actionable Recommendation:

    • Introduce a cross-package security orchestration layer that ensures consistent enforcement of controls like MemoryGuard, PromptInjectionDetector, and PolicyInterceptor.
  3. Enhanced Gap Analysis
    The gap analysis section identifies six concrete gaps but does not prioritize them. Consider ranking these gaps by risk severity and implementation complexity to guide development efforts effectively.

  4. Type Safety and Validation
    While the document does not directly address type safety, ensure that all Pydantic models used for validation (e.g., plugin manifests, A2A task metadata) enforce strict type constraints and reject invalid or malformed inputs.
    Actionable Recommendation:

    • Audit all Pydantic models for missing or overly permissive validation rules.
    • Add unit tests to verify edge cases and invalid input handling.
  5. Thread Safety in Concurrent Execution
    The document does not explicitly address thread safety concerns in multi-agent execution paths. Ensure that shared resources (e.g., memory stores, tool registries) are properly synchronized to prevent race conditions or data corruption.
    Actionable Recommendation:

    • Use thread-safe primitives (e.g., locks, semaphores) or asynchronous patterns to manage shared state.
    • Conduct stress testing under high concurrency to identify potential bottlenecks or race conditions.

Summary

This pull request provides a valuable and detailed mapping of OWASP Agentic Top 10 risks to AGT implementation patterns. However, critical security gaps (e.g., cryptographic binding, sandboxing limitations, and universal auto-wiring) must be addressed to ensure robust compliance and prevent security bypass. Additionally, potential breaking changes should be carefully managed to maintain backward compatibility. The documentation itself is well-written and could be further enhanced with prioritization and future work sections.

Priority Actions:

  • Implement cryptographic binding for A2A communication.
  • Harden sandboxing mechanisms.
  • Refactor BaseIntegration to enforce universal security controls.

Suggested Improvements:

  • Enhance documentation with future work and prioritization.
  • Introduce cross-package security orchestration.
  • Audit type safety and thread safety across the codebase.

This PR is a significant step forward in aligning AGT with OWASP Agentic Top 10 standards, but further work is needed to achieve full compliance and eliminate critical security risks.

imran-siddique added a commit to imran-siddique/agent-governance-toolkit that referenced this pull request Apr 9, 2026
- Migrate copilot-governance from legacy AT identifiers to OWASP ASI 2026
- Add backward-compatible AT→ASI lookup for existing integrations
- Add comprehensive OWASP Agentic Top 10 reference architecture doc
- Add standalone agent-mcp-governance Python package

Supersedes: microsoft#839, microsoft#843, microsoft#844, microsoft#829

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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Closing in favor of #910 which combines this architecture mapping with #844's reference implementation into a single comprehensive document at docs/compliance/owasp-agentic-top10-architecture.md.

The combined doc includes coverage table, Mermaid diagrams, code evidence paths, and honest gap analysis — all using the new ASI 2026 identifiers.

Thank you for the thorough mapping work — it directly informed the combined reference.

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