Multi-Agent Framework for AI System Threat Mitigation
Published:Dec 29, 2025 01:27
•1 min read
•ArXiv
Analysis
This paper addresses the critical and growing problem of security vulnerabilities in AI systems, particularly large language models (LLMs). It highlights the limitations of traditional cybersecurity in addressing these new threats and proposes a multi-agent framework to identify and mitigate risks. The research is timely and relevant given the increasing reliance on AI in critical infrastructure and the evolving nature of AI-specific attacks.
Key Takeaways
- •Identifies specific and emerging threats to AI systems, including LLMs.
- •Proposes a multi-agent framework for threat modeling and mitigation.
- •Highlights the need for ML-specific security frameworks.
- •Emphasizes the importance of dependency hygiene, threat intelligence, and monitoring.
Reference
“The paper identifies unreported threats including commercial LLM API model stealing, parameter memorization leakage, and preference-guided text-only jailbreaks.”