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cs.CR2026

Twin Agent: Context Residual Compression for Privilege Separated Agents

Zhanhao Hu, Dennis Jacob, Xiao Huang +3

Large language model (LLM) agents are vulnerable to security risks, such as prompt injection attacks from untrusted context that manipulate downstream reasoning and tool use. Exist…

cs.CR2026

RECEIPT: Deterministic, Reward-Hacking-Resistant Verification for White-Box Agentic XSS Discovery

Muxi Lyu, Karen Shieh, Yiwei Hou +3

Cross-Site Scripting (XSS) remains one of the most prevalent and damaging classes of web vulnerabilities. LLM-based coding agents offer a promising approach to XSS discovery by com…

cs.CR2026

Revelio: Cost-Efficient Agentic Memory Safety Vulnerability Detection For Repository-Scale Codebases

Yiwei Hou, Hao Wang, Muxi Lyu +6

Memory safety vulnerabilities remain a significant threat even for projects with extensive fuzzing and manual auditing. Recent results suggest that large language models hold great…

cs.CR2026

Parser-Free Querying of Security Logs

Evan Luo, Julien Piet, David Wagner

Security analysts routinely query system logs to detect threats and investigate incidents, but each log source uses its own semi-structured format: logs are cheap to produce, but e…

cs.CR2026

Trojan Hippo: Weaponizing Agent Memory for Data Exfiltration

Debeshee Das, Julien Piet, Darya Kaviani +3

Memory systems enable otherwise-stateless LLM agents to persist user information across sessions, but also introduce a new attack surface. We characterize the Trojan Hippo attack,…

cs.CR2026

Web Agents Should Adopt the Plan-Then-Execute Paradigm

Julien Piet, Annabella Chow, Yiwei Hou +5

ReAct has become the default architecture across LLM agents, and many existing web agents follow this paradigm. We argue that it is the wrong default for web agents. Instead, web a…