OWASP Top 10 for Agentic Applications
The OWASP Agentic Top 10 mapped to real-world attack data and XOR capabilities. A reference page for security teams.
The 10 agentic risks
Each risk category maps to specific attack data from MCPSecBench, Pillar Security, and Snyk ToxicSkills. This page connects the OWASP framework to published research and XOR capabilities.
XOR coverage
XOR addresses risks through verification (output validation), skill scanning (supply chain), MCP integrity checks (protocol security), and signed audit trails (accountability).
The OWASP agentic top 10, mapped to real attack data
Released December 2025, the OWASP Top 10 for Agentic Applications identifies risks specific to AI agent systems, not just models. It extends the LLM Top 10 to cover what happens when models get tools, permissions, and autonomy. Each risk below is mapped to published research and XOR's coverage.
OWASP also maintains a separate MCP Top 10 for protocol-specific risks. Both lists overlap on supply chain and tool integrity. See agent governance for how these risks map to compliance frameworks.
The 10 risks
ASI-01: Agentic Excessive Agency
Agent has more permissions than needed. 58-90% success rate for arbitrary code execution when agents have broad tool access (arXiv:2503.12188). See agent attack landscape.
XOR enforces least-privilege on GitHub permissions. Does not yet control MCP tool permissions at runtime.
ASI-02: Agentic Identity & Access Management
Agents share credentials or escalate privileges. 53% of MCP servers use insecure static secrets; only 8.5% use OAuth (Astrix Security).
XOR uses scoped GitHub App tokens. Flags hardcoded secrets in PR reviews. Does not manage MCP server credentials.
ASI-03: Agentic Prompt Injection
Malicious instructions embedded in data the agent processes. 20% success rate across 2,000+ applications, average time 42 seconds (Pillar Security).
XOR's verification pipeline tests agent-generated patches independently. A poisoned prompt may influence the patch, but verification catches the bad output.
ASI-04: Agentic Supply Chain Vulnerabilities
36.82% of 3,984 agent skills have known vulnerabilities. 76 confirmed malicious payloads in public marketplaces (Snyk ToxicSkills, Feb 2026).
XOR scans agent skills before execution, verifies tool integrity with COSE_Sign1 signatures, and blocks unsigned tools. See building secure skills.
ASI-05: Agentic Uncontrolled Behavior
Agent takes unexpected actions or runs unbounded loops. Multi-agent systems amplify this: one compromised agent propagates across the system.
XOR's guardrail review catches unexpected behavior. Uncertainty stop: XOR says when confidence is low instead of guessing. Patches are verified before merge.
ASI-06: Agentic Knowledge Poisoning
Corrupted training data or poisoned context influences agent decisions. RAG applications are especially vulnerable to indirect injection via retrieved documents.
XOR does not address knowledge poisoning. This requires model-level defenses outside XOR's scope.
ASI-07: Agentic Insecure Output Handling
Agent output is used without validation. 90% of successful jailbreak attacks result in data leakage (Pillar Security). Agent-generated code merged without review is the same class of risk.
This is XOR's core function. Every agent-generated patch is tested against the original vulnerability. Bad patches are rejected before review. See PR verification.
ASI-08: Agentic Excessive Permissions
Agents granted admin access when read-only would suffice. 350% rise in GitHub Actions supply chain attacks in 2025 (StepSecurity), many exploiting over-permissioned workflows.
XOR's Actions hardening pins actions to SHA, reduces workflow permissions to least-privilege, and flags over-permissioned configurations in PR review.
ASI-09: Agentic Insufficient Logging
No audit trail for agent decisions. When something goes wrong, teams can't reconstruct what the agent did or why.
XOR produces signed audit trails for every triage: what was scanned, what passed, what failed, and why. See agent compliance evidence and compliance evidence.
ASI-10: Agentic Multi-Agent Trust
Agents trust other agents without verification. Research shows 58-90% success rates for cross-agent code execution, some configurations reaching 100% (arXiv:2503.12188).
XOR verifies agent output before merge (regardless of which agent produced it). Does not yet verify inter-agent communication at runtime.
Coverage summary
5
Risks covered
ASI-03, 04, 05, 07, 08
3
Partially covered
ASI-01, 02, 10
1
Not addressed
ASI-06
ASI-09 (logging) is also fully covered. XOR addresses 9 of 10 OWASP agentic risks to some degree. Knowledge poisoning (ASI-06) requires model-level defenses that are outside XOR's scope.
Sources
- OWASP Top 10 for Agentic Applications (Dec 2025)
- OWASP MCP Top 10 (2025)
- Snyk ToxicSkills, 3,984 agent skills audited (Feb 2026)
- Pillar Security, State of Attacks on GenAI, 2,000+ apps (Oct 2024)
- Astrix Security, State of MCP Server Security 2025
- arXiv:2503.12188 — Multi-Agent Systems Execute Arbitrary Code
- arXiv:2508.13220 — MCPSecBench: 17 attack types, 4 surfaces
- StepSecurity, 350% rise in GitHub Actions supply chain attacks (2025)
[NEXT STEPS]
Related pages
FAQ
What is the OWASP Top 10 for Agentic Applications?
Released in December 2025, it identifies 10 risk categories specific to AI agent systems including supply chain vulnerabilities, excessive permissions, and uncontrolled agent behavior. It extends the LLM Top 10 to autonomous agents.
How does it differ from the LLM Top 10?
The LLM Top 10 covers model-level risks (prompt injection, training data poisoning). The Agentic Top 10 covers system-level risks: what happens when models get tools, permissions, and autonomy.
Is there a separate OWASP MCP Top 10?
Yes. OWASP also maintains a dedicated MCP Top 10 project covering protocol-specific risks like token mismanagement and shadow MCP servers. See the MCP Security page for details.
Agentic Third-Party Risk
33% of enterprise software will be agentic by 2028. 40% of those projects will be canceled due to governance failures. A risk overview for CTOs.
MCP Server Security
17 attack types across 4 surfaces. 7.2% of 1,899 open-source MCP servers contain vulnerabilities. Technical deep-dive with defense controls.
Open source supply chain risk index
Composite risk ranking of 200 open source projects by ecosystem importance, supply chain risk, downstream reach, and structural context. 585,601 projects scored.
How Verification Works
Test agents on real vulnerabilities before shipping fixes.
Automated Vulnerability Patching
AI agents generate fixes for known CVEs. XOR verifies each fix against the vulnerability before it ships.
Benchmark Results
62.7% pass rate. $2.64 per fix. Real data from 1,920 evaluations.
See which agents produce fixes that work
128 CVEs. 15 agents. 1,920 evaluations. Agents learn from every run.