14 citations · 24 across the 17 of their papers we have counts for
11 papers · 1 filter
CyberGym-E2E: Scalable Real-World Benchmark for AI Agents' End-to-End Cybersecurity Capabilities
Tianneng Shi, Robin Rheem, Dongwei Jiang +13
AI has the potential to transform cybersecurity by enabling systems that can autonomously detect, analyze, and remediate software vulnerabilities. However, existing cybersecurity e…
MAStrike: Shapley-Guided Collusive Red-Teaming on Multi-Agent Systems
Chejian Xu, Zhaorun Chen, Jingyang Zhang +5
Hierarchical multi-agent systems (MAS) are rapidly being deployed in high-stakes workflows across domains such as finance and software engineering. In these systems, safety and sec…
ExploitGym: Can AI Agents Turn Security Vulnerabilities into Real Attacks?
Zhun Wang, Nico Schiller, Hongwei Li +13
AI agents are rapidly gaining capabilities that could significantly reshape cybersecurity, making rigorous evaluation urgent. A critical capability is exploitation: turning a vulne…
The Attack and Defense Landscape of Agentic AI: A Comprehensive Survey
Juhee Kim, Xiaoyuan Liu, Zhun Wang +4
AI agents that combine large language models with non-AI system components are rapidly emerging in real-world applications, offering unprecedented automation and flexibility. Howev…
VulnLLM-R: Specialized Reasoning LLM with Agent Scaffold for Vulnerability Detection
Yuzhou Nie, Hongwei Li, Chengquan Guo +5
We propose VulnLLM-R, the~\emph{first specialized reasoning LLM} for vulnerability detection. Our key insight is that LLMs can reason about program states and analyze the potential…
AgentVigil: Generic Black-Box Red-teaming for Indirect Prompt Injection against LLM Agents
Zhun Wang, Vincent Siu, Zhe Ye +6
The strong planning and reasoning capabilities of Large Language Models (LLMs) have fostered the development of agent-based systems capable of leveraging external tools and interac…
Progent: Securing AI Agents with Privilege Control
Tianneng Shi, Jingxuan He, Zhun Wang +4
AI agents interact with external environments through tool calls, exposing them to attacks like indirect prompt injection that can trigger unauthorized actions. Securing these agen…
Frontier AI's Impact on the Cybersecurity Landscape
Yujin Potter, Wenbo Guo, Zhun Wang +6
The impact of frontier AI (i.e., AI agents and foundation models) in cybersecurity is rapidly increasing. In this paper, we comprehensively analyze this trend through multiple aspe…
Data Free Backdoor Attacks
Bochuan Cao, Jinyuan Jia, Chuxuan Hu +5
Backdoor attacks aim to inject a backdoor into a classifier such that it predicts any input with an attacker-chosen backdoor trigger as an attacker-chosen target class. Existing ba…
LeakAgent: RL-based Red-teaming Agent for LLM Privacy Leakage
Yuzhou Nie, Zhun Wang, Ye Yu +4
Recent studies have discovered that large language models (LLM) may be ``fooled'' to output private information, including training data, system prompts, and personally identifiabl…
TrojFM: Resource-efficient Backdoor Attacks against Very Large Foundation Models
Yuzhou. Nie, Yanting. Wang, Jinyuan. Jia +4
One key challenge in backdoor attacks against large foundation models is the resource limits. Backdoor attacks usually require retraining the target model, which is impractical for…