Cross-Agent Campaign Attribution: Linking Asynchronous Attacks Across LLM Agents
On the map: LLM agents and tool use → Agent threats, guardrails and governance
Abstract (arXiv)
LLM-agent defenses are typically evaluated one session at a time. In deployment, however, attacks can be distributed across independent agents, teams, and runtimes, leaving each local guardrail with only a sparse fragment. We formalize cross-agent asynchronous campaign attribution: linking sessions from the same latent adversarial campaign without shared runtime state, test-time campaign labels, or attacker identity oracles. We introduce Asynchronous Attribution Fingerprint Vectors (A2FV), a lightweight proxy-side reference protocol for scoring pairwise campaign similarity from proxy-observable tool-use, timing, and prompt residue. We also construct SCD-v1, a controlled persona-matched benchmark with benign traffic, isolated attacks, multi-session campaigns, matched non-oracle evasion, and leakage audits. On SCD-v1, A2FV achieves 0.82 pairwise AUC for campaign linking, while score-only adaptations of per-session detectors and chunked LLM judges remain near chance under the same task. The strongest fixed signal is carried by structural and stylometric residue, while timing is retained as a diagnostic channel for richer proxy traces. Crossed-style controls show that the signal is partly style-sensitive but not reducible to style alone. Static and dimension-aware non-oracle stress tests further show that pairwise separability persists under controlled evasion. These results establish cross-agent campaign attribution as a distinct evaluation layer for securing LLM agents in the wild.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 31 of its 54 references that are in the map. First posted on arXiv 21 Jul 2026.
Builds on
31 of 54 references.
Closest ancestors · 15
Same direction, newest first
- 2026Cross-Session Threats in AI Agents: Benchmark, Evaluation, and Algorithms22 Apr
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- 2026Cross-Layer Semantic Flow Reconstruction for Attack Detection in Agentic Systems4 Mar
- 2026AgentSentry: Mitigating Indirect Prompt Injection in LLM Agents via Temporal Causal Diagnostics and Context Purification26 Feb
- 2025A Multi-Agent LLM Defense Pipeline Against Prompt Injection Attacks16 Sep
- 2025AgentSight: System-Level Observability for AI Agents Using eBPF2 Aug
- 2025DRIFT: Dynamic Rule-Based Defense with Injection Isolation for Securing LLM Agents13 Jun
- 2025WASP: Benchmarking Web Agent Security Against Prompt Injection Attacks22 Apr
- 2025textitAgents Under Siege: Breaking Pragmatic Multi-Agent LLM Systems with Optimized Prompt Attacks31 Mar
- 2025Defeating Prompt Injections by Design24 Mar
- 2025Multi-Agent Systems Execute Arbitrary Malicious Code15 Mar
3 more
Other references · 16
- 2023AgentBench: Evaluating LLMs as Agentscited by 1,267
- 2023Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversationscited by 1,207
- 2026DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligencecited by 1,040
- 2025DataSentinel: A Game-Theoretic Detection of Prompt Injection Attackscited by 143
- 2025Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agentscited by 71
- 2025PromptShield: Deployable Detection for Prompt Injection Attackscited by 50
- 2024AgentOps: Enabling Observability of LLM Agentscited by 46
- 2025A Critical Evaluation of Defenses against Prompt Injection Attackscited by 36
- 2023Kairos: Practical Intrusion Detection and Investigation using Whole-system Provenancecited by 31
- 2025Securing the Model Context Protocol (MCP): Risks, Controls, and Governancecited by 23
- 2021threaTrace: Detecting and Tracing Host-based Threats in Node Level Through Provenance Graph Learningcited by 22
- 2025Prompt Injection Detection and Mitigation via AI Multi-Agent NLP Frameworkscited by 15
4 more
- 2023NODLINK: An Online System for Fine-Grained APT Attack Detection and Investigationcited by 14
- 2025Masks and Mimicry: Strategic Obfuscation and Impersonation Attacks on Authorship Verificationcited by 7
- 2024FP-Inconsistent: Measurement and Analysis of Fingerprint Inconsistencies in Evasive Bot Trafficcited by 1
- 2025BinaryShield: Cross-Service Threat Intelligence in LLM Services using Privacy-Preserving Fingerprintscited by 1
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