Attack Surface
Security research involving prompt handling and processing
5 matching entries out of 610 in this category
Context assembly can promote repository, tool or skill content into higher-priority instructions or persistent state. The paper studies 12 pinned agent-harness versions.
Source: arXiv
A stored interaction can later steer a memory-augmented agent's answer without direct memory-store access. The study evaluates persistent response manipulation in MemoryOS and MemGPT.
Source: arXiv
MemSecBench follows malicious agent-memory content from initial write through persistence, retrieval, action selection, execution, and attempted selective repair. Its controlled Write–Execute–Forget protocol evaluates 310 human-reviewed cases across two harnesses, four memory backends, three model backends, and seven evidence-gated lifecycle checkpoints.
Source: arXiv
AgentS4D measures unsafe actions and state changes across complete workspace-agent executions rather than treating task completion or isolated model responses as safety evidence. Its 328 sandboxed cases introduce risky content through user requests, documents, web resources, tools, third-party skills, and persistent memory, then compare the same cases across four agent harnesses and five model backends.
Source: arXiv
HANDBOOK.md measures whether an agent can apply detailed organizational rules while completing realistic, multi-step enterprise tasks. The vendor-authored benchmark includes 65 resettable MCP-backed workflows, policy documents of 20 to 124 pages, and 824 deterministic rubric checks covering required decisions, prohibited actions, and final environment state.
Source: arXiv