Victor Mattos, Guilherme Sobreira, Amit Bhaya, Alan Oliveira de Sá, Charles B. Prado, Raphael Machado, Gaurav Srivastava, Daniel S. Menasche
原文摘要
This paper studies covert system identification in cyber-physical systems, where an attacker infers plant dynamics while attempting to remain stealthy. We introduce a lightweight static linear model in which the time to execute a setpoint change is proportional to the commanded speed difference. On a Siemens PROFINET testbed this gain captures the drive's ramp response accurately but not its terminal overshoot. That omitted transient becomes the attacker's main exposure: on real traces, the residual left by the linear masking procedure is detected by two anomaly monitors, Process-Aware Stealthy-Attack Detection (PASAD) and the CUSUM change detector. Across the cascade identification and staircase attack strategies defined in the paper, the stealth boundary relates to the plant's own settling transients, a structural limit for this class of models under the adopted detector-calibration regime. Comparison against dynamic models obtained by N4SID, trained at multiple excitation amplitudes, shows that the evaluated N4SID models do not improve stealth over the structure-informed ramp model. Since per-cycle computation is negligible for both procedures, the binding trade-off is not inference cost but whether the attacker can acquire and refresh a model without injecting detectable perturbations.
Agents on the Patch Path: Mining AI-Associated Security Remediation in GitHub Security Advisories
Claudio Segal, Paulo Segal, Hudson Silva Borges, Paulo Silveira, Daniel S. Menasché, Eduardo Santana de Almeida, Felipe de Sant'Anna Paixão, Joanna C. S. Santos 等 11 位作者
Autonomous coding agents are increasingly involved in software development, but their participation in real-world security remediation remains poorly understood. We investigate observable evidence of coding-agent involvement in commits referenced by reviewed GitHub Security Advisories (GHSAs), characterize the associated vulnerabilities, and examine review timing in relation to advisory disclosure routes. We develop a local-Git mining pipeline that identifies agent evidence through known agent accounts, explicit commit-message attribution, repository instruction files, and agent configuration artifacts. Linking these findings to GHSA-referenced commits yields 469 valid candidate advisories across 189 repositories, including 448 with direct attribution and 21 supported only by repository-level evidence. Claude is the most frequently observed tool family, followed by Copilot and Cursor. We further compare review timing against other advisories and historical baselines using an exact route--timing decomposition. Relative to advisories published in the same months, candidate advisories exhibit a modestly longer mean review lag, primarily associated with within-route timing rather than disclosure-route composition. Compared with a historical baseline, they exhibit substantially shorter mean review lags, with both route composition and within-route timing contributing to the difference. These findings demonstrate that coding-agent involvement is already observable across hundreds of security advisories and repositories, including high- and critical-severity vulnerabilities. By distinguishing direct attribution from repository-level adoption evidence and separating route-composition effects from within-route timing differences, we provide an empirical characterization of agent-associated remediation in the GHSA review pipeline.