Chameleon is an openly distributed adaptive honeypot that integrates: a BiLSTM classifier achieving 99.61% accuracy across seven threat categories at ~2 ms CPU latency; a locally deployed Qwen3.5-0.8B model delivering 90% generation accuracy at 4.5 ms latency; and two meta-heuristic engines.
R. Swami, Tusharkumar Singh, Akash Warde et al.· 0 citations
Generative models now write application code, harden and monitor it, and probe it for exploitable flaws, so that one family of models increasingly plays builder, defender and breaker at once. The prevailing view treats full autonomy as the natural end point of assistance. This article argues for a narrower and more def...
This paper presents a formal model of an OT intrusion response use case using the POMDP framework, which includes a realistic model of partial observability that is based on traffic measurements and allows for tractable, learning-based solution methods for automated intrusion response, which are based on PPO.
It is shown that preserving or reinjecting the weight offset fails to maintain protection, indicating that preventative steering relies on active adaptation rather than a static defense, and proposes Progressive Intensity Scheduling (PIS), which improves safety robustness over static-strength steering while reducing ha...
Jing Guan, Ya-Chao Yang, Zhao-Liang Liu et al.· 0 citations
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A feasibility taxonomy of twenty inference-time mechanisms across monitoring, verification, and enforcement, each rated on a four-point readiness scale against a documented four-vendor evidence base is developed.
A controlled evaluation of adversarial transferability across 60 detectors spanning six backbones, two pretraining regimes, and five training-data configurations establishes source-model selection as central dimensions of credible transfer-based black-box robustness evaluation.
Rafael M. Mamede, Pedro C. Neto, A. F. Sequeira· 0 citations
The audit shows that synthetic releases leak subgroup membership and that prior attacks systematically underestimate this leakage, and that DP is effective at the aggregate level: it substantially reduces average leakage at every budget the authors test.
Yi-Dan Sun, Viktor Schlegel, Srinivasan Nandakumar et al.· 0 citations
CS-Guard, the first benchmark to systematically evaluate guardrails for code generation security, finds that current guardrails perform poorly against malicious code-generation re- quests, and uses a modular three-layer guardrail taxonomy that lets developers register guardrails for evaluation.
Directional ablation removes an aligned language model's ability to refuse by projecting a single"refusal direction" out of the weights that write the residual stream, and whether it survives the shift to frontier mixture-of-experts (MoE) models whose residual streams are no longer a single tensor and whose weights shi...
Federated learning shares model updates rather than raw data, yet these updates can be inverted to reconstruct the clients'training data. Analytic reconstruction attacks, which invert a gradient in closed form, degrade as the batch grows: prior single-round attacks recover only about half of a batch of size $100$ even...
It is argued that neither the objections nor the assurances can currently be verified and that this unverifiability, rather than watermarking itself, is the substantive governance failure.
Alexander Nemecek, Vipin Chaudhary, Erman Ayday· 0 citations
Large language model safety and security research is preoccupied with, among other things, detecting and preventing jailbreak attacks: alignment bypasses that allow an adversarial user to elicit unwanted or harmful outputs from models. Arbitrary cipher, or covert communication, attacks are one such type of jailbreak an...
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.
