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OWASP Published its First Top 10 for AI Agents
The Web Application Security Consortium (OWASP) has released the first formal risk taxonomy for autonomous AI agents, known as Agentic Applications. This document is titled “Top 10 for Agentic Applications” and was published in December 2025.
Contextual Numbers:
- 88% of enterprises reported having security incidents involving AI agents within the last year (based on a survey conducted by Gravitee, with 919 respondents).
- Only 21% of enterprises have runtime visibility into what their agents are doing.
- 82% of enterprises have unknown agents in their environments as of April 2026 (according to the Cloud Security Alliance).
- 5.5% of public MCP servers contain poisoned tool descriptions, with an attack success rate of 84.2% when auto-approval is enabled.
Top 10 Risks for AI Agents:
- ASI01 – Agent Goal Hijack: A malicious prompt was used to trick a GitHub agent into exfiltrating data from private repositories. The agent appeared to function normally throughout the incident.
- ASI02 – Tool Misuse: An unauthorized regex operation led to the export of 45,000 customer records via one valid tool call, despite having permission to query individual records.
- ASI03 – Identity and Privilege Abuse: Agents inherit permissions from their users. Compromising one agent in a delegation chain exposes all user permissions within that chain.
- ASI04 – Supply Chain Compromise: OX Security identified 7,000 vulnerable MCP servers with over 150M downloads affected by architectural flaws in Anthropic‘s MCP SDKs across multiple programming languages.
- ASI05 – Unexpected Code Execution: Check Point demonstrated Remote Code Execution (RCE) via poisoned `.claude` configuration files, allowing agents to execute commands with full developer permissions.
- ASI06 – Memory Poisoning: Galileo AI found that a compromised agent could cause 87% of downstream decision-making processes within four hours in multi-agent systems. This was demonstrated against multiple models including ChatGPT, Gemini, and Claude.
- ASI07 – Insecure Inter-Agent Communications: Multi-agent systems communicate via message buses without authentication, enabling attacks where an agent acts as a middleman.
- ASI08 – Cascading Failures: Errors in natural language processing can propagate quickly within agent chains before being caught by human reviewers.
- ASI09 – Human-Agent Trust Exploitation: Agents can present a clean summary like “approve this data export” to unsuspecting humans, with the real action coming from compromised or manipulated agents.
- ASI10 – Rogue Agents: These are AI counterparts of insider threats. Actions appear legitimate but result from malicious agents that have been trained to mimic normal behavior over time.
The risk taxonomy highlights how these 10 risks form a kill chain, where one vulnerability leads to an escalation of control through subsequent steps. For example, goal hijacking can lead to tool misuse, which in turn enables code execution and memory poisoning.
Key Takeaways
- The number of security incidents involving AI agents has significantly increased over the past year.
- Runtimes lack visibility into what agents are doing, making it difficult to detect or respond to issues promptly.
- Sophisticated attacks like memory poisoning and unexpected code execution highlight the need for robust security measures in multi-agent systems.
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Originally published at reddit.com. Curated by AI Maestro.
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