The results demonstrate that augmenting conventional flow features with temporal information yields consistent gains; binary detection improves by up to 3% in F1 score, while macro-averaged multi-class F1 increases by approximately 27%, with the most significant improvements occurring in attack classes with pronounced...
Majed Luay, S. Layeghy, Niloufar Noorbin et al.· IEEE Access· 21 citations
This study unveils the capability of attackers to generate adversarial policies even when restricted to partial observations of the victims in multi-agent competitive environments, and proposes a novel black-box attack (SUB-PLAY) that incorporates the concept of constructing multiple subgames to mitigate the impact of...
Oubo Ma, Yuwen Pu, L. Du et al.· Conference on Computer and C...· 16 citations
SingProbe is introduced, a lightweight intrinsic runtime guard that directly reuses hidden states produced during LLM inference and operates alongside autoregressive decoding and extends this paradigm to medical generation through SingProbe-Med, which selectively activates risk-directed decoding interventions only when...
Findings suggest that P3M should be viewed as a lightweight empirical protocol for examining privacy-utility-safety trade-offs rather than as a formal privacy guarantee or a defense against extraction attacks.
Di-Shu Yang, Jing-Jing Liu, Ji-Ze Li· 1 citation
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This work presents an open-source transformer implementation for uncropped full-key attacks which uses the standard transformer encoder backbone, adapting only the input and output layers to the side-channel setting.
Adversarial Robustness with Manifold-Oriented Training (ARMOR), a novel defense that realizes the core insights of on-manifold adversarial training (OMAT) in low-data regimes and translates insights from manifold-based training to defend object detectors amidst training data scarcity.
Hao-Ran Wang, Matthew Lau, Alec Helbling et al.· 0 citations
A benchmark score is a joint property of the model, the evaluation harness, the elicitation budget, the sampled population, and contamination status. Leaderboards publish the model and the score, so capability and leakage stay observationally equivalent. Existing taxonomies classify contamination for automated detectio...
Johanna Angulo, Víctor Yeste, H. Espinós-Morató· 0 citations
This work proposes a secure multi-party computation framework that enables the training and inference of CellCnn entirely on secret-shared data, and preserves accuracy close to its plaintext counterpart while outperforming the prior privacy-preserving baseline.
S. S. Magara, Esther Havemann, Debora Jutz et al.· 0 citations
CallosumNet is a spatio-temporal graph unlearning framework biologically inspired by the corpus callosum structure that reconstructs subgraphs using biologically-inspired virtual edges and restores interlinked spatio-temporal dependencies among subgraphs via a lightweight meta-graph integration layer.
Qi-Ming Guo, Wenbo Sun, Chen Pan et al.· 1 citation
This work formalizes resulting Hessian collection as a partially symmetric decomposition to establish conditions for local identifiability and stability to exploit vector-output stencil reuse to reduce the structural query cost by a factor of 16.
Five domain-specific RAG applications developed for real-world scenarios across governance, cybersecurity, agriculture, industrial research, and medical diagnostics are presented, highlighting technical, operational, and ethical challenges affecting the reliability and usability of RAG systems in practice.
M. Hasan, Muhammad Waseem, Kai-Kristian Kemell et al.· EUROMICRO Conference on Soft...· 10 citations· ⚡1
Securing AI systems is not a task any single discipline can accomplish alone; it requires sustained collaboration between machine learning researchers, cybersecurity professionals, and policymakers if AI technologies are to remain reliable, trustworthy, and resilient in adversarial environments.
Harsh Verma· International Journal of Sci...· 1 citation
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.
