45 citations · 95 across the 23 of their papers we have counts for
27 papers
Cost-Efficient Theorem Proving via Agent Orchestration in Program Verification
Shuangjie Yao, Nikolaus Holzer, Mark Paul Santolucito +3
Program verification establishes software correctness through machine-checkable proofs constructed in theorem provers. It's a guarantee especially valuable for code generated by la…
Cheap to Hypothesize, Costly to Verify: The Defense Surface of Agentic Vulnerability Discovery
Kaikai Zhang, Zihan Zhang, Yuchong Xie +4
Autonomous LLM agents turn vulnerability discovery into a repository-scale search: they generate many vulnerability hypotheses but can verify only a subset under a finite budget. W…
Similarity Is Not Validity: Defending LLM Semantic Caches Against Poisoning
Zihan Zhang, Shuangjie Yao, Zesen Liu +8
Semantic caches reduce LLM serving costs by reusing previously generated answers for semantically similar queries. However, retrieval is based solely on embedding similarity betwee…
Compositional Safety Failures in Harness Evolution: Identification and Runtime Monitoring
Zhixiang Zhang, Zesen Liu, Wai Ip Lai +2
Self-evolving agent harnesses continually update persistent components such as memory, prompts, skills, and tools. We call this process harness evolution. However, such evolution c…
Safe to Check, Unsafe to Use: Relinking at the Compression Boundary of LLM Agents
Zesen Liu, Zihan Zhang, Dongdong She
Summarization-based prompt compression is increasingly used by LLM agents to shorten long, distributed contexts, but it shifts the security boundary: filters inspect the pre-compre…
Machine learning inference of fission yields from gamma spectroscopy for very low-yield nuclear test verification
Julien de Troullioud de Lanversin, Jiehui Li, Christopher Fichtlscherer +2
Very low-yield nuclear tests pose a major verification challenge for the zero-yield standard of the Comprehensive Nuclear-Test-Ban Treaty (CTBT). The zero-yield standard prohibits…
Rewriting the Response Path: Silent Tampering and Provider-Signed Defense in BYOK LLM Agents
Mingyu Luo, Zihan Zhang, Zesen Liu +7
LLM agents convert model outputs into consequential actions, including communications, code changes, and financial transactions. Developers often trust evidence such as test result…
RandSet: Randomized Corpus Reduction for Fuzzing Seed Scheduling
Yuchong Xie, Kaikai Zhang, Yu Liu +4
Seed explosion is a fundamental problem in fuzzing seed scheduling, where a fuzzer maintains a huge corpus and fails to choose promising seeds. Existing works focus on seed priorit…
On Protecting Agentic Systems' Intellectual Property via Watermarking
Liwen Wang, Zongjie Li, Yuchong Xie +4
The evolution of Large Language Models (LLMs) into agentic systems that perform autonomous reasoning and tool use has created significant intellectual property (IP) value. We demon…
From Similarity to Vulnerability: Key Collision Attack on LLM Semantic Caching
Zhixiang Zhang, Zesen Liu, Yuchong Xie +2
Semantic caching has emerged as a pivotal technique for scaling LLM applications, widely adopted by major providers including AWS and Microsoft. By utilizing semantic embedding vec…
QueryIPI: Query-agnostic Indirect Prompt Injection on Coding Agents
Yuchong Xie, Zesen Liu, Mingyu Luo +7
Modern coding agents integrated into IDEs orchestrate powerful tools and high-privilege system access, creating a high-stakes attack surface. Prior work on Indirect Prompt Injectio…
When Compression Becomes an Attack Surface: Black-Box Attacks on Prompt-Compressed LLM Agents
Zesen Liu, Zhixiang Zhang, Yuchong Xie +1
Prompt compression is increasingly deployed in LLM agents to reduce latency and cost, but it also determines what the backend LLM ultimately sees. We show that, when trusted and un…
Red-Teaming Coding Agents from a Tool-Invocation Perspective: An Empirical Security Assessment
Yuchong Xie, Mingyu Luo, Zesen Liu +7
Coding agents powered by large language models are becoming central modules of modern IDEs, helping users perform complex tasks by invoking tools. While powerful, tool invocation o…
ZTaint-Havoc: From Havoc Mode to Zero-Execution Fuzzing-Driven Taint Inference
Yuchong Xie, Wenhui Zhang, Dongdong She
Fuzzing is a widely used technique for discovering software vulnerabilities, but identifying hot bytes that influence program behavior remains challenging. Traditional taint analys…
Empc: Effective Path Prioritization for Symbolic Execution with Path Cover
Shuangjie Yao, Dongdong She
Symbolic execution is a powerful program analysis technique that can formally reason the correctness of program behaviors and detect software bugs. It can systematically explore th…
RouthSearch: Inferring PID Parameter Specification for Flight Control Program by Coordinate Search
Siao Wang, Zhen Dong, Hui Li +3
Flight control programs use PID control modules with user-configurable Proportional (P), Integral (I), and Derivative (D) parameters to manage UAV flying behaviors. Users can adjus…
Extracting Formal Specifications from Documents Using LLMs for Automated Testing
Hui Li, Zhen Dong, Siao Wang +4
Automated testing plays a crucial role in ensuring software security. It heavily relies on formal specifications to validate the correctness of the system behavior. However, the ma…
Comment on Revisiting Neural Program Smoothing for Fuzzing
Dongdong She, Kexin Pei, Junfeng Yang +2
MLFuzz, a work accepted at ACM FSE 2023, revisits the performance of a machine learning-based fuzzer, NEUZZ. We demonstrate that its main conclusion is entirely wrong due to severa…
FOX: Coverage-guided Fuzzing as Online Stochastic Control
Dongdong She, Adam Storek, Yuchong Xie +3
Fuzzing is an effective technique for discovering software vulnerabilities by generating random test inputs and executing them against the target program. However, fuzzing large an…
NeuDep: Neural Binary Memory Dependence Analysis
Kexin Pei, Dongdong She, Michael Wang +6
Determining whether multiple instructions can access the same memory location is a critical task in binary analysis. It is challenging as statically computing precise alias informa…
: Rigorous and Efficient Directed Greybox Fuzzing
Abhishek Shah, Dongdong She, Samanway Sadhu +3
Directed greybox fuzzing is a popular technique for targeted software testing that seeks to find inputs that reach a set of target sites in a program. Most existing directed greybo…
Effective Seed Scheduling for Fuzzing with Graph Centrality Analysis
Dongdong She, Abhishek Shah, Suman Jana
Seed scheduling, the order in which seeds are selected, can greatly affect the performance of a fuzzer. Existing approaches schedule seeds based on their historical mutation data,…
MTFuzz: Fuzzing with a Multi-Task Neural Network
Dongdong She, Rahul Krishna, Lu Yan +2
Fuzzing is a widely used technique for detecting software bugs and vulnerabilities. Most popular fuzzers generate new inputs using an evolutionary search to maximize code coverage.…
Fine Grained Dataflow Tracking with Proximal Gradients
Gabriel Ryan, Abhishek Shah, Dongdong She +2
Dataflow tracking with Dynamic Taint Analysis (DTA) is an important method in systems security with many applications, including exploit analysis, guided fuzzing, and side-channel…