English

Cross-Agent Campaign Attribution: Linking Asynchronous Attacks Across LLM Agents

Cryptography and Security 2026-07-21 v1 Artificial Intelligence

Abstract

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 (A2FVA^2FV), 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, A2FVA^2FV 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.

Cite

@article{arxiv.2607.18826,
  title  = {Cross-Agent Campaign Attribution: Linking Asynchronous Attacks Across LLM Agents},
  author = {SangJin Park and Myungsub Choi and Jineok Kim and Minseung Kang},
  journal= {arXiv preprint arXiv:2607.18826},
  year   = {2026}
}

Comments

22 pages, 5 figures. Accepted at the Second Workshop on Agents in the Wild: Safety, Security, and Beyond (AIWILD) at ICML 2026