AI & ML
Correct Answers, Unsupported Findings: Evidence Binding in Forensic Reconstruction of LLM Agent Logs
arXiv cs.CR0 views
arXiv:2610.09581v1 Announce Type: new Abstract: Forensic reconstruction of LLM-agent actions requires not only recovering the correct value, but establishing which preserved record supports that finding. Tool logs, generated explanations, and local citation identifiers capture different parts of this evidence, yet a citation identifier does not establish a source unless its binding to a record is preserved. We audit this distinction using 64 mechanically checkable cases from saved AgentDojo Banking executions. Two LLM readers reconstruct source relationships under controlled variations in visible evidence and identifier-to-record bindings. We separately evaluate complete-record agreement, evidence-grounded findings, justified abstention, and unsupported assertions. With original identifiers and no binding table, Sonnet recovered every literal source location but made unsupported citation-source assertions in 26 of 28 cases requiring the missing relation; 22 nevertheless matched the complete reference. Adding explicit bindings improved grounded reconstruction for both readers, whereas identifier renaming alone provided no consistent remedy. A deterministic same-packet comparator correctly resolved the bounded task or abstained throughout. These results show that factual agreement alone is insufficient for evaluating forensic reconstruction of agent logs and motivate preserving explicit record bindings to distinguish supported findings from correct guesses.
Source:https://arxiv.org/abs/2610.09581PreviousMARS: Malware Analysis with Rule-Based Scoring of LLM ClaimsNextAutomotive Hardware Attacks: An Architect's Guide to TARA
Related posts
HR manager LinkedIn About section examples you can adapt
0dev.to Career
🚀 Ready to explore the world of AWS Cloud and build something amazing together? ☁️
0dev.to Programming
The Second Scrape Should Be Almost Free
0dev.to Programming
Updates to Authtics Advisories
0dev.to Programming
My betting model looked great for 3,671 predictions. It never beat the bookmaker.
0dev.to TypeScript

Build efficient agents with knowledge distillation on Amazon SageMaker AI
0AWS Machine Learning Blog