Agent-Fence: Mapping Security Vulnerabilities Across Deep Research Agents

Large language models are increasingly deployed as *deep agents* that plan, maintain persistent state, and invoke external tools, shifting safety failures from unsafe text to unsafe *trajectories*. We introduce **AgentFence**, an architecture-centric security evaluation that defines 14 trust-boundary attack classes spanning planning, memory, retrieval, tool use, and delegation, and detects failures via *trace-auditable conversation breaks* (unauthorized or unsafe tool use, wrong-principal actions, state/objective integrity violations, and attack-linked deviations). Holding the base model fixed, we evaluate eight agent archetypes under persistent multi-turn interaction and observe substantial architectural variation in mean security break rate (MSBR), ranging from 0.29 ± 0.04 (LangGraph) to 0.51 ± 0.07 (AutoGPT). The highest-risk classes are operational: Denial-of-Wallet (0.62 ± 0.08), Authorization Confusion (0.54 ± 0.10), Retrieval Poisoning (0.47 ± 0.09), and Planning Manipulation (0.44 ± 0.11), while prompt-centric classes remain below 0.20 under standard settings. Breaks are dominated by boundary violations (SIV 31%, WPA 27%, UTI+UTA 24%, ATD 18%), and authorization confusion correlates with objective and tool hijacking (ρ ≈ 0.63 and ρ ≈ 0.58). AgentFence reframes agent security around what matters operationally: whether an agent stays within its goal and authority envelope over time.