Yu Feng

This paper systematically analyzes the threat posed by malicious third-party API routers in the LLM supply chain, finding that a significant number of routers actively perform payload injection, credential theft, and cryptocurrency draining.

The paper introduces AgentFlow, a novel framework that uses a typed graph DSL and feedback-driven optimization to automatically synthesize and improve multi-agent harnesses for discovering security vulnerabilities.

Semia is a novel static auditor that translates complex, prose-defined agent skills into a verifiable Datalog fact base, enabling the detection of critical security vulnerabilities in real-world LLM agents.

This paper demonstrates a novel attack against the shuffling defense used in secure Transformer inference, showing that randomly permuted activations can still be exploited to recover model weights.

The paper introduces Conleash, a client-side middleware that uses a risk lattice to enforce granular, boundary-scoped authorization for tool invocations, significantly improving user consent and security.

The paper introduces Sefz, a semantic fuzzing framework that automatically discovers specification violations in LLM agent skills, finding a significant number of previously unknown exploitable guardrail breaches.

The paper proposes ReuseRL, a method that improves agent generalization in Reinforcement Learning by enforcing structural compressibility of successful agent trajectories into reusable skills.

The paper introduces ERGeoBench, a comprehensive diagnostic benchmark designed to evaluate the fine-grained capabilities of multimodal large language models (MLLMs) for embodied geo-localization across various viewing conditions.