Owasp Agentic
MicrosoftOfficialPublished by Microsoft
- Skill
- Guidelines
- Security
- CC-BY-SA-4.0
- No issues found
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The owasp-agentic skill provides a machine-readable implementation of the OWASP Top 10 for Agentic Applications (2026). It serves as a comprehensive reference for developers and security professionals to understand, detect, and mitigate critical vulnerabilities inherent in autonomous AI agent systems. The skill is organized into a vulnerability index and individual reference documents for each of the ten identified risk categories, offering actionable guidance on prevention, detection, and remediation.
What it does
- Provides structured, machine-readable definitions for the OWASP Agentic Top 10 security risks.
- Offers vulnerability checklists to help identify potential security gaps in agentic workflows.
- Includes prevention controls and remediation strategies for each identified vulnerability.
- Details attack scenarios to illustrate how agentic systems can be exploited.
- Facilitates integration into security auditing and threat modeling processes for AI agents.
When to use it
- During the design and architecture phase of building an AI agent system.
- When performing security audits or threat modeling on existing agentic applications.
- When developing guardrails or safety protocols for autonomous AI agents.
- When training development teams on secure AI agent implementation practices.
Advantages
- Standardized security framework based on industry-recognized OWASP research.
- Machine-readable format allows for automated integration into agent development pipelines.
- Provides concrete, actionable remediation steps rather than just theoretical risks.
- Covers a wide range of agent-specific threats like goal hijacking and rogue agents.
Usage examples
- “What are the primary risks associated with Agent Goal Hijack according to the OWASP Agentic Top 10?”
- The agent retrieves the ASI01:2026 reference, explaining that goal hijack involves attackers manipulating an agent's objectives or decision pathways, and lists associated risks like unauthorized data exfiltration and financial fraud.
- “Provide a checklist to assess if my agent is vulnerable to Tool Misuse and Exploitation.”
- The agent references ASI02:2026 to provide a list of conditions and patterns that indicate potential tool misuse, along with recommended prevention controls like enforcing least privilege.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/microsoft/owasp-agentic/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip owasp-agentic.zip -d .agents/skills
Documentation by Microsoft
OWASP® Agentic Top 10 — Skill Entry
This SKILL.md is the entrypoint for the OWASP Agentic Top 10 skill.
The skill encodes the OWASP Top 10 for Agentic Applications (2026) as structured, machine-readable references that an agent can query to identify, assess, and remediate security risks in AI agent systems.
Normative references (Agentic Top 10)
Skill layout
SKILL.md— this file (skill entrypoint).references/— the Agentic Top 10 normative documents.00-vulnerability-index.md— index of all vulnerability identifiers, categories, and cross-references.01through10— one document per vulnerability aligned with OWASP Agentic Security numbering.
Third-Party Attribution
Copyright © OWASP Foundation. OWASP® Top 10 for Agentic Applications (2026) content is derived from works by the OWASP Foundation, licensed under CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/). Source: https://genai.owasp.org/resource/owasp-top-10-for-agentic-applications-for-2026/ Modifications: Vulnerability descriptions restructured into agent-consumable reference documents with added detection and remediation guidance. OWASP® is a registered trademark of the OWASP Foundation. Use does not imply endorsement.
Files
- ATTRIBUTION.mdAdded by AutoGineer981 B
- LICENSEAdded by AutoGineer17.9 KiB
- SKILL.md2.6 KiB
- references/00-vulnerability-index.md3.1 KiB
- references/01-agent-goal-hijack.md6.4 KiB
- references/02-tool-misuse-and-exploitation.md6.1 KiB
- references/03-identity-and-privilege-abuse.md6.4 KiB
- references/04-agentic-supply-chain-vulnerabilities.md5.9 KiB
- references/05-unexpected-code-execution.md6.0 KiB
- references/06-memory-and-context-poisoning.md5.8 KiB
- references/07-insecure-inter-agent-communication.md6.4 KiB
- references/08-cascading-failures.md6.4 KiB
- references/09-human-agent-trust-exploitation.md6.5 KiB
- references/10-rogue-agents.md5.9 KiB
references/07-insecure-inter-agent-communication.md
title: 'ASI07: Insecure Inter-Agent Communication' description: OWASP Agentic Top 10 reference for insecure inter-agent communication vulnerabilities in multi-agent systems
07 Insecure Inter-Agent Communication
Identifier: ASI07:2026 Category: Communication Security
Description
Multi-agent systems depend on continuous communication between autonomous agents that coordinate via APIs, message buses, and shared memory, significantly expanding the attack surface. Decentralized architecture, varying autonomy, and uneven trust make perimeter-based security models ineffective. Weak inter-agent controls for authentication, integrity, confidentiality, or authorization let attackers intercept, manipulate, spoof, or block messages.
Insecure inter-agent communication occurs when these exchanges lack proper authentication, integrity, or semantic validation, allowing interception, spoofing, or manipulation of agent messages and intents. The threat spans transport, routing, discovery, and semantic layers, including covert or side-channels where agents leak or infer data through timing or behavioral cues.
This differs from ASI03 (Identity and Privilege Abuse), which focuses on credential and permissions misuse, and ASI06 (Memory and Context Poisoning), which targets stored knowledge corruption. ASI07 focuses on compromising real-time messages between agents.
Risk
Interception and manipulation of real-time messages between agents to inject hidden instructions.
Spoofing of agent identities or capabilities to redirect sensitive data through attacker infrastructure.
Replay of delegation or trust messages to trick agents into granting unauthorized access.
Protocol downgrade attacks that coerce agents into weaker communication modes.
Cross-context contamination through modified or injected messages that blur task boundaries.
Behavioral profiling through metadata analysis of traffic patterns, enabling prediction and manipulation of agent behavior.
Conflicting agent actions from semantic divergence in message interpretation.
Vulnerability checklist
Inter-agent messages are transmitted over unencrypted channels enabling man-in-the-middle semantic manipulation.
Modified or injected messages can blur task boundaries between agents, leading to data leakage or goal confusion.
Delegation or trust messages lack replay protection, allowing stale instructions to be honored.
Agents can be coerced into weaker communication modes or accept spoofed agent descriptors.
Discovery and coordination traffic can be misdirected to forge relationships with malicious agents.
Traffic patterns reveal decision cycles and relationships, enabling behavioral profiling and manipulation.
Prevention controls
Use end-to-end encryption with per-agent credentials and mutual authentication. Enforce PKI certificate pinning, forward secrecy, and regular protocol reviews.
Digitally sign messages, hash both payload and context, and validate for hidden or modified natural-language instructions. Apply natural-language-aware sanitization and intent-diffing.
Protect all exchanges with nonces, session identifiers, and timestamps tied to task windows. Maintain short-term message fingerprints to detect cross-context replays.
Disable weak or legacy communication modes. Require agent-specific trust negotiation and bind protocol authentication to agent identity.
Reduce the attack surface for traffic analysis by using fixed-size or padded messages where feasible, smoothing communication rates, and avoiding deterministic schedules.
Define and enforce allowed protocol versions. Reject downgrade attempts or unrecognized schemas and validate that both peers advertise matching capability and version fingerprints.
Authenticate all discovery and coordination messages using cryptographic identity. Secure directories with access controls and verified reputations.
Use registries that provide digital attestation of agent identity, provenance, and descriptor integrity. Require signed agent cards and continuous verification before accepting messages.
Example attack scenarios
Scenario A — Semantic injection via unencrypted communications
Over HTTP or other unauthenticated channels, a MITM attacker injects hidden instructions, causing agents to produce biased or malicious results while appearing normal.
Scenario B — Trust poisoning via message tampering
In an agentic trading network, altered reputation messages skew which agents are trusted for decisions.
Scenario C — Context confusion via replay
Replayed emergency coordination messages trigger outdated procedures and resource misallocation.
Scenario D — Agent-in-the-Middle via descriptor poisoning
A malicious endpoint advertises spoofed agent descriptors or false capabilities. When trusted, it routes sensitive data through attacker infrastructure.
Scenario E — Semantics split-brain
A single instruction is parsed into divergent intents by different agents, producing conflicting but seemingly legitimate actions.
Detection guidance
Monitor for anomalous routing flows in discovery and coordination channels.
Detect protocol downgrade attempts or unrecognized schema versions in inter-agent exchanges.
Track message replay attempts through nonce, session identifier, and timestamp validation.
Analyze traffic patterns for signs of behavioral profiling or deterministic communication schedules.
Validate that peers consistently advertise matching capability and version fingerprints.
Monitor for unsigned or unattested agent cards in discovery registries.
Remediation
Enforce end-to-end encryption with mutual authentication on all inter-agent communication channels.
Deploy digital signatures on all messages with payload and context hashing.
Implement nonce-based and timestamp-bound replay protection on all exchanges.
Disable weak or legacy communication modes and enforce protocol version pinning.
Remove unverified or suspicious entries from discovery registries and require signed agent cards.
Use versioned, typed message schemas with explicit per-message audiences and reject failing validations.
Content derived from works by the OWASP Foundation, licensed under CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0/). Modifications: Restructured into agent-consumable reference format with added detection and remediation guidance.
Requirements and compatibility
Written for: GitHub Copilot, Claude Code
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Claude Code frontmatter
Security
No issues found by our static scan· No issues found
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Published by
- Microsoft
- Publisher
- Microsoft (Official)
- Source
- microsoft/hve-core · .github/skills/security/owasp-agentic · commit f425e94
- Licence
- Creative Commons Attribution-ShareAlike 4.0 International (redistribution allowed)
- LICENSE (written by AutoGineer)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Microsoft.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download owasp-agentic.zip