Agentic AI Threat Modeling - Part I

✨ Agentic AI Threat Modeling ✨

Agents, Agents, Agents .... they are everywhere, or at least it seems so.
We all want to deploy them, leverage their capabilities, or not feel left out. (FOMO) 👀

👉 Well, regardless of where you fall in this distribution, securing agents would become important sooner or later, so adding it to your plan doesn't hurt! And threat modeling AI agents is an area that affords depths of insights.

FACT -> Most security folks don't need to go down the agentic architecture rabbit hole ... but it is imperative to grasp enough knowledge to get the job done. 👍

🤺 Thankfully, the OWASP GenAI Security Project folks recently released a document that provides unbeatable knowledge around agentic security -> "Agentic AI - Threats and Mitigations". You don't want to miss out; head over for your free copy ->https://genai.owasp.org/resource/agentic-ai-threats-and-mitigations/

The publication's example agentic threat model has 15 threats ( T1 - T 15). I summarized the first four in the attached document with additional references, including mitigations.

Please have a look and let me know your thoughts. 🙌

Which security use cases of AI agents excite you most?
How do you secure the agents you use today?

Transcript

1. Agentic AI Threat Modeling Part I @run2obtain Excerpts from The Agentic AI Threat & Mitigations (OWASP GenAI Security Project) 1
2. @run2obtain Source: https://genai.owasp.org/resource/agentic-ai-threats-and-mitigations/ Agentic Threat Modeling Summary 2 T1 – T4 are discussed in next slides; description and mitigations. The remaining threats (T5 – T15) will be covered the upcoming parts, watch out !
3. @run2obtain Source: https://genai.owasp.org/resource/agentic-ai-threats-and-mitigations/ T1: Memory Poisoning Memory poisoning involves exploiting an AI's memory systems, both short and long- term, to introduce malicious or false data and exploit the agent’s context. This can lead to altered decision-making and unauthorized operations. 3 https://billchan226.github.io/AgentPoison.html Description
4. @run2obtain Source: https://genai.owasp.org/resource/agentic-ai-threats-and-mitigations/ T1: Memory Poisoning ü Memory content validation ü Session isolation ü robust authentication mechanisms for memory access ü Anomaly detection systems ü Regular memory sanitization routines. ü Require AI-generated memory snapshots for forensic analysis and rollback if anomalies are detected. 4 Mitigations
5. @run2obtain Source: https://genai.owasp.org/resource/agentic-ai-threats-and-mitigations/ T2: Tool Misuse Tool Misuse occurs when attackers manipulate AI agents to abuse their integrated tools through deceptive prompts or commands, operating within authorized permissions. This includes Agent Hijacking. 5 Description https://www.linkedin.com/feed/update/urn:li:activity:7311147680393539586/
6. @run2obtain Source: https://genai.owasp.org/resource/agentic-ai-threats-and-mitigations/ T2: Tool Misuse ü Enforce strict tool access verification. ü Monitor tool usage patterns ü Validate agent instructions, and set clear operational boundaries to detect and prevent misuse. ü Implement execution logs that track AI tool calls for anomaly detection and post-incident review. 6 Mitigations
7. @run2obtain Source: https://genai.owasp.org/resource/agentic-ai-threats-and-mitigations/ T3: Privilege Compromise Privilege compromise arises when attackers exploit weaknesses in permission management to perform unauthorized actions. This often involves dynamic role inheritance or misconfigurations. Example is the Confused deputy attack. 7 Description https://www.linkedin.com/feed/update/urn:li:activity:7311900565431635968/
8. @run2obtain Source: https://genai.owasp.org/resource/agentic-ai-threats-and-mitigations/ T3: Privilege Compromise ü Implement granular permission controls, dynamic access validation, robust monitoring of role changes, and thorough auditing of elevated privilege operations. ü Prevent cross-agent privilege delegation unless explicitly authorized through predefined workflows 8 Mitigations
9. @run2obtain Source: https://genai.owasp.org/resource/agentic-ai-threats-and-mitigations/ T4: Resource Overload Resource overload targets the computational, memory, and service capacities of AI systems to degrade performance or cause failures, exploiting their resource-intensive nature. Attackers can intentionally trigger resources overload to maliciously disrupt normal operations. 9 Description https://arxiv.org/pdf/2403.16971 AIOS: LLM Agent Operating System
10. @run2obtain Source: https://genai.owasp.org/resource/agentic-ai-threats-and-mitigations/ T4: Resource Overload ü Deploy resource management controls, implement adaptive scaling mechanisms, establish quotas, and monitor system load in real-time to detect and mitigate overload attempts. ü Implement AI rate-limiting policies to restrict high-frequency task requests per agent session. ü Test your agents to understand operational limits make necessary adjustments 10 Mitigations
11. @run2obtain Watch out for Part II … meanwhile, checkout how Mitigant secures GenAI workloads https://www.mitigant.io/en/platform/security-for-genai 11 Attack Emulation for Amazon Bedrock
12. @run2obtain Leverage The Mitigant Advantage 12 https://www.mitigant.io/en/platform/cloud-attack-emulation Start your free trial TODAY - https://www.mitigant.io/en/sign-up Free Trial