← Back to all news
arXiv Computation and Language By Isadora White, Yasaman Jafari, Taylor Berg-Kirkpatrick

Black-Box Forensics for Conversational LLM Agents

Read the original on arXiv Computation and Language →

The paper presents methods for black-box forensic analysis of conversational large language model (LLM) agents, focusing on two tasks: attribution—identifying the base model powering a chatbot—and fingerprinting—detecting identical system prompts across different endpoints. Attribution classifiers achieve 98% accuracy using only a few non‑adversarial conversation turns, while a cross‑encoder fingerprinting approach attains an AUC of 0.768 and an F1 of 0.703 on unseen prompts, improving to 0.943 AUC when aggregating 50 interactions. These techniques enable investigators to trace AI‑enabled scams back to their underlying models and link related scams through shared system prompts.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

Related stories

arXiv Machine Learning
Sep 25

A Corpus of Real Scam- and Spam-Call Conversations from an Active Voice-Agent Honeypot

The paper introduces a dataset of 10,015 real scam and spam phone calls collected over 53 days using an active voice‑agent honeypot. Each call is recorded, transcribed, and automatically labeled, yielding 328,869 turn‑level transcripts and 895 hours of audio from 5,665 distinct numbers. The corpus distinguishes between predatory‑but‑legal lead generation and outright scams, with labels validated by human review and technical checks on realism.

By Ethan Traister, Dennis Tsang Ng, Siyu Zhang, Huaiyu Guo, Tommy Duong, Tyler Wu, Yuchen Zhou, Xingyu Shen, Jiaqi Wu, Simiao Ren
agentsbenchmarks
More like this →
arXiv Machine Learning
Aug 26

Anatomy of a Scam Call: What 10,000 real scam and spam calls reveal about how phone scammers operate

The study analyzes 10,211 real scam and spam calls collected by an AI voice‑agent honeypot, revealing that scammers operate on a templated, office‑hour schedule and use disposable numbers to recycle scripts. Callers predominantly seek identity anchors such as home addresses and dates of birth, and the amount of conversation increases with the target’s age, though the requested information remains unchanged. Early detection is feasible, with escalation predictability reaching 0.87 ROC‑AUC by the eighth line using simple bag‑of‑words models.

By Ethan Traister, Ankit Raj, Jiaqi Gan, Xingyu Shen, Tyler Wu, Yuchen Zhou, Tommy Duong, Kidus Zewde, Siying Chen, Simiao Ren
llmsagentsfine-tuningbenchmarks
More like this →
arXiv AI
Sep 25

On the Effectiveness of Kernel-Level Evidence for Agent Security

The paper introduces the Agent Cross‑Layer Evidence (ACE) corpus, pairing application‑level telemetry with kernel‑level syscall traces to study agent security. It shows that kernel evidence alone is discriminative and that combining it with application‑level data outperforms either layer alone, revealing complementary signals. The study also demonstrates that this cross‑layer approach generalizes to unseen attack families and works across different agent runtimes.

By Spencer King, Zhilu Zhang, Mikhail Kuznetsov, Kay Liu, Baris Coskun, Wei Ding
llmsagentsbenchmarks
More like this →