OWASP LLM Top 10: ASI02 Tool Misuse

## Risk/Vuln Name
**Tool Misuse**

**Author(s):**
OWASP Agentic Security Initiative Team

### Description
Attackers exploit the dynamic integration and enhanced autonomy of agentic AI systems to misuse authorized tools. Unlike traditional LLM applications, agents maintain memory, dynamically decide which tools to invoke, and may delegate actions to other agents. This increases the risk of adversarial misuse through chaining, privilege escalation, or execution of unintended actions.

The threat is partially covered by [LLM06:2025 Excessive Agency](https://genai.owasp.org/llmrisk/llm062025-excessive-agency/), but agent-based systems pose unique challenges due to memory persistence and multi-agent delegation. 

### Common Examples of Risk
1. Tool chaining allows indirect access to restricted functions or data exfiltration.
2. Prompt injection modifies the input to tools, such as editing a payload sent to APIs.
3. Autonomous agents trigger external actions using tools without proper human oversight.
4. RAG agents misuse tools to retrieve poisoned or misleading content.

### Prevention and Mitigation Strategies
1. Implement strict tool access control policies and limit which tools agents can execute.
2. Require function-level authentication before an AI can use a tool.
3. Use execution sandboxes to prevent AI-driven tool misuse from affecting production systems.
4. Use rate-limiting for API calls and computationally expensive tasks.
5. Restrict AI tool execution based on real-time risk scoring.
6. Implement just-in-time (JIT) access for AI tool usage, revoking permissions after use.
7. Log all AI tool interactions with forensic traceability.
8. Detect command chaining that circumvents security policies.
9. Enforce explicit user approval for AI tool executions involving financial, medical, or administrative functions.
10. Maintain detailed execution logs for auditing and anomaly detection.
11. Require human verification before executing AI-generated code with elevated privileges.
12. Detect abnormal tool execution frequency and flag repetitive misuse.
13. Monitor AI tool interactions for unintended side effects that may impact security posture.

### Example Attack Scenarios
- **Scenario 1: Parameter Pollution Exploitation** – An attacker discovers and manipulates an AI booking system’s function call, tricking it into reserving 500 seats instead of one, causing financial loss.
- **Scenario 2: Tool Chain Manipulation** – An attacker exploits an AI customer service agent by chaining tool actions, extracting high-value customer records, and sending them via an automated email system.
- **Scenario 3: Automated Tool Abuse** – An AI document processing system is tricked into generating and mass-distributing malicious documents, unknowingly executing a large-scale phishing attack.
- **Scenario 4: Tool Poisoning** – A RAG agent is tricked into retrieving tool-generated context from a malicious source. The poisoned tool output influences subsequent tool use, causing unsafe actions such as overwriting databases.
- **Scenario 5: Agent Hijacking via Tools** – As described by NIST, a malicious prompt triggers the agent to misuse a file upload tool and data extraction utility, enabling unintended file access and exfiltration without crossing policy boundaries.

### Reference Links
1. [Agentic AI - Threats and Mitigations](https://genai.owasp.org/resource/agentic-ai-threats-and-mitigations/)
2. [LLM06:2025 Excessive Agency](https://genai.owasp.org/llm-top-10/LLM06-excessive-agency)
3. [LLM03:2025 Supply Chain Vulnerabilities](https://genai.owasp.org/llm-top-10/LLM03-training-data-supply-chain)
4. [NIST Blog on Agent Hijacking](https://www.nist.gov/news-events/news/2025/01/technical-blog-strengthening-ai-agent-hijacking-evaluations)