arXiv AI

Public-Sharing Labels and Verbatim Field Egress in an MCP-to-A2A Agent Configuration: A Controlled Multi-Model Study

The study evaluates safety properties of a controlled MCP-to-A2A agent configuration by measuring verbatim field egress across ten record scenarios under three labeling conditions (CONFIDENTIAL, no header, PUBLIC – OK TO SHARE). Using four models repeated four times each, 480 trials were conducted, and the results show that adding a PUBLIC header is descriptively linked to higher verbatim egress, with the effect varying strongly by model. The study releases code, byte‑pinned traces, and an offline analysis pipeline as a public artifact.

arXiv AI
2d ago

CASCADE: A Component Ablation and Corpus Audit of a Layered Local Defense for MCP-Based Systems

The paper evaluates CASCADE, a fully local layered defense for Model Context Protocol (MCP)-based systems, by conducting a component ablation and corpus audit on a fixed 5,000-sample dataset. It demonstrates that the choice of aggregation convention heavily influences reported metrics, that detection performance varies with provenance, and that the released configuration does not fully disclose the operating point. The study also shows that a local review model invoked for a third of requests does not alter classification outcomes, highlighting the importance of reproducibility and transparency in defense evaluations.

By \.Ipek Abas{\i}kele\c{s} Turgut, Edip G\"um\"u\c{s}
arXiv AI
Aug 24

Calibrating Criterion Revision in LLM Agents: Failure Modes and a Trace-Anchored Protocol

The paper introduces a framework for evaluating how large language model agents revise their success criteria after failures, defining five non‑compensatory conditions that must be met for a criterion revision to be considered valid. Using the CMB‑0.1 protocol, the authors test twelve cross‑domain scenarios across four system configurations, finding that no model trial satisfies all five conditions and highlighting specific failure modes such as zero‑state reconstruction and inadequate intervention sensitivity. They propose a more stringent trace‑anchored CMB‑0.4 protocol to better isolate and measure criterion revision in future studies.

By Guodong Xu
arXiv Machine Learning
Aug 5

DP-MemView: A Memory Interface for Attribute-Level Transcript Privacy in Long-Term LLM Agents

arXiv:2608. 03130v1 Announce Type: cross Abstract: Long-term memory enables persistent personalization in LLM agents, but repeated memory-conditioned responses can cumulatively reveal protected attributes even when they are never stated explicitly.

By Jong Wook Kim, Byoungjae Min, Kennedy Edemacu, Yoonhyuk Choi, Sae-Hong Cho, Beakcheol Jang
arXiv AI
Jun 3

Which Defense Closes Which Threat? Attributing OWASP-LLM-Top-10 Coverage and Its Brittleness Under Paraphrasing

arXiv:2606. 02822v1 Announce Type: cross Abstract: Production LLM applications stack several defense families -- refusal-phrase filters, token-budget controls, model allowlists, rate limits, tool-registry authentication -- yet existing breach-and-attack-simulation (BAS) benchmarks report a single aggregate coverage number, hiding which family closes which threat.

By Alexandre Cristov\~ao Maiorano