Accurately evaluating adversarial robustness is a longstanding challenge. A flawed attack design can inflate robustness estimates, making deployment risk assessment and defense comparison unreliable. Historically, standardized attacks such as AutoAttack have largely resolved this for image classifiers, providing a reli...
Vincent Limbach, Jonas Dornbusch, David L\"udke et al.· 0 citations
Test Vector Leakage Assessment (TVLA) is widely used for side-channel leakage detection, but its reliance on Welch's t-test makes it primarily sensitive to differences in the means of two leakage populations. Consequently, TVLA may fail to detect leakage that manifests through changes in other characteristics of the un...
J\'an Mikulec, Jakub Breier, Xiaolu Hou· 0 citations
Mixture-of-Experts (MoE) language models introduce unique challenges for safety alignment due to their sparse routing mechanisms, which can enable degenerate optimization behaviors under standard full-parameter fine-tuning. In our preliminary experiments, we observe that naively applying full-parameter safety fine-tuni...
Jiacheng Liang, Yuhui Wang, Tanqiu Jiang et al.· 0 citations
Vertical localization, particularly floor separation, remains a major challenge in indoor positioning systems operating in GPS-denied multistory environments. This paper proposes a fully data-driven, graph-based framework for blind floor separation using only Wi-Fi fingerprint trajectories, without requiring prior buil...
Rabia Yasa Kostas, Kahraman Kostas· 0 citations
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The expansion of text-to-image diffusion models has raised concerns about harmful outputs, from fabricated depictions of public figures to sexually explicit imagery. To mitigate such risks, prior work has proposed concept erasure methods that aim to sever unwanted concepts from the model via fine-tuning, yet it remains...
Tobias Braun, Jonas Henry Grebe, Patrick Mohr et al.· 0 citations
Instruction tuning aligns large language models (LLMs) with human intentions but requires diverse, high-quality data that are difficult to collect in privacy-sensitive domains. Federated instruction tuning (FedIT) enables collaborative training across data owners, yet existing methods typically assume sufficient local...
Zhuo Zhang, Jingyuan Zhang, Jintao Huang et al.· 0 citations
Many machine learning applications involve sensitive data and therefore require training under differential privacy (DP). However, DP training often degrades model utility. In some cases, first pre-training the model on"public"data before finetuning with DP on the sensitive data can reduce the drop in utility. However,...
Yu-Fei Chen, Tejumade Afonja, Anvith Thudi et al.· 0 citations
The release decision for frontier AI systems increasingly relies on cyber capability benchmarks, yet public vulnerability benchmarks can expose agents to previously published advisories, exploits, and fixes, making it difficult to distinguish prior exposure from capability on unseen vulnerabilities. We evaluate open-we...
Tobias Heldt, Matthew Turk, Christoph R. Landolt et al.· 0 citations
IoT encompasses diverse physical entities, from smart home devices to autonomous vehicles, creating a complex environment with heterogeneous security models. This heterogeneity makes IoT sub-systems vulnerable to various network attacks. Modern security systems must therefore be more robust to ensure security and priva...
Tool-using language-model agents are vulnerable to indirect prompt injection because they must act on untrusted external content. Existing training-time defenses can reduce attack success rates, but often at the cost of general capabilities. We show that training-based defenses induce substantial drift in the model's o...
Mohamed Dhouib, Clément Elliker, Alexi Canesse et al.· 1 citation
Security Awareness Gap Evaluation (SAGE), a trace-based method that combines an assessment of the security reasoning recorded at each turn with the reconstructed code history to identify the earliest turn at which a repair diverges from the task's security intent, is proposed.
Wen-Jie Bai, Muhammad Waseem, Z. Rasheed et al.· 0 citations
The robustness of Personally Identifiable Information (PII) protection in Large Language Models (LLMs) is a critical concern, yet the risks associated with cross-lingual data extraction remain under-explored. This study evaluates the vulnerability of English-centric and multilingual models to Training Data Extraction (...
Alexandru Nazare, Agnese Profico, Nicol\`o Vania et al.· 0 citations
Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.
Critical flaws, compromised release channels and agentic AI threats landed within days of each other in mid-October 2026. Why “Black October” may say more about how well we now find weaknesses than about the attacks themselves. The post Black October? The Week AI Changed the Cybersecurity Conversation appeared first on GPT-Lab.
