Owasp Agentic
MicrosoftOfficialPublished by Microsoft
- Skill
- Guidelines
- Security
- CC-BY-SA-4.0
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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://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/08-cascading-failures.md
title: 'ASI08: Cascading Failures' description: OWASP Agentic Top 10 reference for cascading failure vulnerabilities where single agent failures propagate through multi-agent systems
08 Cascading Failures
Identifier: ASI08:2026 Category: Resilience
Description
Agentic cascading failures occur when a single fault (hallucination, malicious input, corrupted tool, or poisoned memory) propagates across autonomous agents, compounding into system-wide harm. Because agents plan, persist, and delegate autonomously, a single error can bypass stepwise human checks and persist in a saved state. As agents form emergent links to new tools or peers, these latent faults chain into privileged operations that compromise confidentiality, integrity, and availability.
Cascading failures describes the propagation and amplification of an initial fault, not the initial vulnerability itself, across agents, tools, and workflows, turning a single error into system-wide impact.
Observable symptoms include rapid fan-out where one faulty decision triggers many downstream agents, cross-domain or tenant spread beyond the original context, oscillating retries or feedback loops between agents, and downstream queue storms or repeated identical intents.
Risk
System-wide harm from propagation of a single fault across autonomous agents and workflows.
Compromised confidentiality, integrity, and availability through latent fault chains reaching privileged operations.
Automated execution of unsafe steps without validation due to planner-executor coupling.
Persistent error propagation through corrupted long-term memory that influences new plans and delegations.
Governance drift from weakened human oversight after repeated success leading to unchecked configuration changes.
Feedback-loop amplification where agents relying on each other's outputs magnify initial errors.
Catastrophic defensive actions from propagation of hallucinated or injected false alerts across multi-agent systems.
Vulnerability checklist
A hallucinating or compromised planner emits unsafe steps that the executor automatically performs without validation.
Poisoned long-term goals or state entries continue influencing new plans and delegations after the original source is removed.
A single corrupted update can cause peer agents to act on false alerts or reboot instructions across regions.
One agent's misuse of an integration or elevated credential leads downstream agents to repeat unsafe actions.
A poisoned or faulty release pushed by an orchestrator propagates automatically to all connected agents.
Human oversight weakens after repeated success and bulk approvals propagate unchecked configuration drift.
Two or more agents rely on each other's outputs, creating a self-reinforcing loop that magnifies initial errors.
Prevention controls
Design systems with zero-trust fault tolerance that assumes availability failure of agentic function components and external sources.
Sandbox agents with least privilege, network segmentation, scoped APIs, and mutual auth to contain failure propagation.
Issue short-lived, task-scoped credentials for each agent run and validate every high-impact tool invocation against a policy-as-code rule before executing it.
Separate planning and execution via an external policy engine to prevent corrupt planning from triggering harmful actions.
Implement checkpoints, governance agents, or human review for high-risk actions before agent outputs are propagated downstream.
Implement blast-radius guardrails such as quotas, progress caps, and circuit breakers between planner and executor.
Re-run recorded agent actions in an isolated clone to test whether sequences trigger cascading failures. Gate policy expansion on replay tests passing predefined blast-radius caps.
Example attack scenarios
Scenario A — Financial trading cascade
Prompt injection poisons a market analysis agent, inflating risk limits. Position and execution agents auto-trade larger positions while compliance stays blind to within-parameter activity.
Scenario B — Healthcare protocol propagation
Supply chain tampering corrupts drug data. Treatment auto-adjusts protocols, and care coordination spreads them network-wide without human review.
Scenario C — Cloud orchestration breakdown
Poisoning in resource planning adds unauthorized permissions. Security applies them, and deployment provisions backdoored, costly infrastructure without per-change approval.
Scenario D — Auto-remediation feedback loop
A remediation agent suppresses alerts to meet latency SLAs. A planning agent interprets fewer alerts as success and widens automation, compounding blind spots across regions.
Scenario E — Agentic cyber defense hallucination
Propagation of a hallucinated imminent attack is propagated in underlying multi-agent systems, causing unnecessary but catastrophic defensive actions such as shutdowns, denials, and network disconnects.
Detection guidance
Detect fast-spreading commands across agent networks and throttle or pause on anomalies.
Track decisions against baselines and alignment and flag gradual degradation.
Monitor for rapid fan-out where one faulty decision triggers many downstream agents in a short time.
Detect cross-domain or tenant spread of faults beyond the original context.
Identify oscillating retries or feedback loops between agents and downstream queue storms.
Record all inter-agent messages, policy decisions, and execution outcomes in tamper-evident, time-stamped logs bound to cryptographic agent identities.
Remediation
Deploy circuit breakers between planner and executor to halt cascading propagation.
Isolate affected agents with least privilege and network segmentation to contain failure spread.
Roll back corrupted persistent memory and state entries using version-controlled snapshots.
Revoke task-scoped credentials for compromised agent runs and re-validate all downstream actions.
Restore human oversight checkpoints for any governance drift identified during the cascade.
Maintain lineage metadata for every propagated action to support forensic traceability and rollback validation.
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
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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
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Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download owasp-agentic.zip