Machine learning-based intrusion detection systems (IDS) are increasingly used in resource-constrained Internet of Things (IoT) environments, yet their robustness is often evaluated against static attacks rather than adversaries that adapt to detection feedback. This paper investigates adaptive port-scan evasion agains...
Logan Andrew North, Priya Sanjay Kaluskar, Shasi Kumar Ramachandran Prabhu et al.· 0 citations
PatchBench, a benchmark of empirically observed model-specific jailbreak failures inducing actionable harmful answers, and PatchBench-Local, an evaluation protocol testing whether a patch is behaviourally precise, provide a more precise basis for developing and comparing jailbreak repair methods.
Alexi Canesse, Mathis Le Bail, Maël Jenny et al.· 0 citations
Intrusion detectors can confidently misclassify attacks that were not seen during training. Human review can correct these errors, but only a limited number of cases can be checked. Uncertainty-based review may overlook confident errors, while anomaly scores alone do not show whether changing the review plan will corre...
Tabular data underpin prediction and decision-making in medicine, finance, government and science, but often contain sensitive individual-level information, creating a need for accurate prediction while preserving privacy. Traditional private learning provides formal privacy guarantees, but requires slow dataset-specif...
Talal Alrawajfeh, Cristiana-Diana Diaconu, Ossi Räisä et al.· 0 citations
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DP-SGD protects training data by adding Gaussian noise to clipped gradients. The amount of noise is usually chosen by running a numerical privacy accountant inside a search. We study DP-SGD with random allocation, where each epoch uses every record once, at a randomly chosen step. For this setting we give a one-line fo...
Murat Bilgehan Ertan, Marten van Dijk· 0 citations
Does removing harmful information make open-weight models resistant to fine-tuning attacks? We show that mutual information at release alone cannot universally certify slow recovery. Function-preserving reparameterizations leave information unchanged while altering gradient-descent geometry, so an invariant certificate...
Domenic Rosati, Alessa Carbo, Ali Dadsetan et al.· 0 citations
Backdoor attacks pose a serious threat to large language models (LLMs) by causing otherwise benign systems to produce attacker-specified malicious behavior when a hidden trigger is present. In this work, we study post hoc detoxification of backdoored LLMs in a practical setting where the defender has access to the pois...
Arash Raftari, Mehrdad Mahdavi, Nathan Blackthorn et al.· 0 citations
LLM-based Agents are becoming increasingly capable and widely deployed, creating growing incentives for adversarial misuse in the real-world. A key emerging threat is Decomposition Attacks \cite{glukhov2024breach, jones2024adversaries} in which a harmful task is broken into simpler, benign subtasks that evade safety me...
Vikhyath Kothamasu, Virginia Smith, Chhavi Yadav· 0 citations
World models have recently seen a rapid growth in both their popularity and capability as more data efficient tools for generating robot training data or simulating real world environments, with many works proposing their integration into the robot learning pipeline. While highly practical, in this work we demonstrate...
Ethan Rathbun, Ahmed Agha, Saaduddin Mahmud et al.· 0 citations
Model providers increasingly release the weights of large language models. Although these models are safety-aligned before release, their safeguards can often be removed by fine-tuning on harmful data. A growing class of defenses aims to make alignment robust to such malicious fine-tuning, but these defenses are typica...
Itay Zloczower, Eyal Lenga, Gilad Gressel et al.· 0 citations
Retrieval-augmented generation (RAG) improves large language models (LLMs) with external knowledge, but this access path creates security risks distinct from inherent prompt-only or parametric-model flaws. We frame secure RAG as securing external knowledge access. We conducted a systematic search and curated 135 works...
Yuming Xu, Mingtao Zhang, Zhuohan Ge 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.
