Ying Li

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.

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 argues that LLM agent security is fundamentally an agent-human interaction (AHI) problem, demonstrating that industry practices rely on human-centric mechanisms while academic research focuses on undeployed approaches.

The paper proposes AuthGraph, a dual-graph defense framework that structurally compares information provenance (what data was used) against a clean authorization baseline to detect fine-grained, parameter-source-level injection attacks on LLM agents.

The paper introduces CEON, a Circular Economy Ontology Network, designed to improve semantic interoperability and knowledge representation across diverse industry sectors throughout the product life cycle.

The paper introduces CEON, a Circular Economy Ontology Network, designed to improve semantic interoperability and knowledge representation across diverse industry sectors throughout the product life cycle.

This paper presents EvoVuln, an automated framework that synthesizes and refines detection logic for smart contract vulnerabilities using minimal labeled samples.

This paper develops a formal semantics and verification methods for a core continuous-variable quantum programming language, using closed positive quadratic forms as semantic predicates.

This paper presents a closed-loop framework called LAW that integrates language reasoning, action generation, and world modeling for long-horizon manipulation tasks.