研究仓库

LongPIBench introduces a benchmark for prompt-injection attacks and defenses in four document-centric long-context workflows: paper peer review, resume screening, email summarization, and code review. It combines synthetic and real-world datasets, evaluates heuristic and optimization-based attacks across eight language models, and measures both prevention-based attack success and detection-based false-positive and false-negative rates. The paper reports that attacks remain effective and that defenses that perform well on short-context benchmarks often degrade on long documents, with additional ablations over document format, injection position, attack goals, context length, and segmentation.

Skill-augmented agents persist loaded skills across execution loops, creating severe vulnerabilities where malicious or compromised skills steer tool calls, leak secrets, or corrupt state after installation. Static pre-installation vetting cannot detect these task-conditioned threats. To address this challenge, the authors introduce Defense-as-Skill, deploying the runtime guard as an installable, inspectable, and editable skill named SkillSonar that dynamically gates sensitive actions through allow, replan, or confirmation decisions. They construct SCOPE-R, a benchmark spanning six risk families with 206 attack-confirmed malicious instances and 43 benign tasks, and optimize SkillSonar using feedback-driven Monte Carlo Tree Search. On repeated GLM-5 evaluations, SkillSonar substantially curtails attack success while preserving benign task utility across multiple agent harnesses.