activity
20232026
most citedBadChain: Backdoor Chain-of-Thought Prompting for Large Language Models

8 citations · 30 across the 24 of their papers we have counts for

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Showing cs.CRShow all

7 papers · 1 filter

cs.CR2026

CyberChainBench: Can AI Agents Secure Smart Contracts Against Real-World On-Chain Vulnerabilities?

Jintao Huang, Fengqing Jiang, Radha Poovendran +1

We present CyberChainBench, a benchmark for evaluating LLM-based agents on smart contract security across three complementary tasks: vulnerability detection, exploit generation, an…

cs.CR2025

BadScientist: Can a Research Agent Write Convincing but Unsound Papers that Fool LLM Reviewers?

Fengqing Jiang, Yichen Feng, Yuetai Li +3

The convergence of LLM-powered research assistants and AI-based peer review systems creates a critical vulnerability: fully automated publication loops where AI-generated research…

cs.CR2024

ACE: A Model Poisoning Attack on Contribution Evaluation Methods in Federated Learning

Zhangchen Xu, Fengqing Jiang, Luyao Niu +3

In Federated Learning (FL), a set of clients collaboratively train a machine learning model (called global model) without sharing their local training data. The local training data…

cs.CR2024

ChatBug: A Common Vulnerability of Aligned LLMs Induced by Chat Templates

Fengqing Jiang, Zhangchen Xu, Luyao Niu +2

Large language models (LLMs) are expected to follow instructions from users and engage in conversations. Techniques to enhance LLMs' instruction-following capabilities typically fi…

cs.CR2024★ 3 cited

SafeDecoding: Defending against Jailbreak Attacks via Safety-Aware Decoding

Zhangchen Xu, Fengqing Jiang, Luyao Niu +3

As large language models (LLMs) become increasingly integrated into real-world applications such as code generation and chatbot assistance, extensive efforts have been made to alig…

cs.CR2024★ 8 cited

BadChain: Backdoor Chain-of-Thought Prompting for Large Language Models

Zhen Xiang, Fengqing Jiang, Zidi Xiong +3

Large language models (LLMs) are shown to benefit from chain-of-thought (COT) prompting, particularly when tackling tasks that require systematic reasoning processes. On the other…

cs.CR2023★ 4 cited

Identifying and Mitigating Vulnerabilities in LLM-Integrated Applications

Fengqing Jiang, Zhangchen Xu, Luyao Niu +4

Large language models (LLMs) are increasingly deployed as the service backend for LLM-integrated applications such as code completion and AI-powered search. LLM-integrated applicat…