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新论文 · v1arXiv:2611.12579

Covert System Identification Under Timing Constraints: A Static Linear Model and Its Detectability Implications

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.

eess.SY · cs.CR

新论文 · v1arXiv:2611.09808

补丁路径上的智能体:在 GitHub 安全公告中挖掘与 AI 相关的安全修复

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

AI 速读 基于摘要

作者通过本地 Git 挖掘流程,在经审核的 GitHub Security Advisories(GHSA)所引用的提交中识别编码智能体参与安全修复的可观察证据,并比较相关公告的审核时延。

cs.SE

查看原文摘要

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.

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.27ACL Anthology · ACL 2026

Hindsight: Structured Agent Memory that Retains, Recalls, and Reflects

Christopher Latimer, Nicolò Boschi University of Bologna, Andrew Neeser Washington Post, Chris Bartholomew Vectorize, Gaurav Srivastava, Xuan Wang Virginia Polytechnic Institute and State University, Naren Ramakrishnan Virginia Tech

原文摘要

We demonstrate Hindsight, a working memory system for AI agents that organizes long-term memory into four logical networks and exposes three core operations. The world, experience, observation, and opinion networks separate objective facts from subjective beliefs, giving developers visibility into what an agent knows versus what it believes. The retain, recall, and reflect operations handle ingestion, retrieval, and reasoning respectively, with a parallel pipeline that combines vector search, keyword matching, graph traversal, and temporal filtering, backed by PostgreSQL with pgvector. Unlike existing systems such as MemGPT, Zep, and Mem0, Hindsight is the only one that jointly provides fact-belief separation, temporal entity graphs, evolving opinions with confidence scores, and configurable behavioral profiles. On LongMemEval and LoCoMo, Hindsight with a 20B open-source model reaches 83.6% and 83.2% accuracy, outperforming full-context GPT-4o and all prior memory systems; with Gemini-3 Pro, LongMemEval accuracy reaches 91.4%. Our interactive demo lets users build memory graphs through multi-session conversations, inspect how memories are classified, and watch opinions form and change. The system is open-source under the MIT license, available as a Python package (pip install hindsight-all) and Docker image, with 13.3K GitHub stars and 763 forks to date, and in production use at Fortune 500 enterprises. Video demo: https://youtu.be/4M2wS-yEmVA.

cs.CL · cs.AI

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1275ACL Anthology · ACL 2026

SoundBreak: A Systematic Study of Audio-Only Adversarial Attacks on Trimodal Models

Aafiya Shamshad Hussain Virginia Polytechnic Institute and State University, Gaurav Srivastava, Alvi Md Ishmam Argonne National Laboratory, Zaber Ibn Abdul Hakim Virginia Polytechnic Institute and State University, Chris Thomas

原文摘要

Multimodal foundation models that integrate audio, vision, and language achieve strong performance on reasoning and generation tasks, yet their robustness to adversarial manipulation remains poorly understood. We study a realistic and underexplored threat model: untargeted, audio-only adversarial attacks on trimodal audio–video–language models. We analyze six complementary attack objectives that target different stages of multimodal processing, including audio encoder representations, cross-modal attention, hidden states, and output likelihoods. Across four state-of-the-art models and multiple benchmarks, we show that audio-only perturbations can induce severe multimodal failures, achieving up to 96% attack success rate. We further show that attacks can be successful at low perceptual distortions (LPIPS \leq 0.08, SI-SNR \geq 0 dB) and benefit more from extended optimization than increased data scale. We evaluate the feasibility of these attacks under physically realistic conditions by incorporating room impulse response (RIR) modeling, showing that audio-only perturbations remain effective under environmental transformations and thus highlight the practical risk of single-modality attacks in real-world multimodal systems. Transferability across models and encoders remains limited, while speech recognition systems such as Whisper primarily respond to perturbation magnitude, achieving >97% attack success under severe distortion. These results expose a previously overlooked single-modality attack surface in multimodal systems and motivate defenses that enforce cross-modal consistency. Our project website is available at https://aafiya-h.github.io/soundbreak/.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表