X-SG$^2$S is the first framework to unify 1D-to-3D watermarking and enable simultaneous multi-modal watermark embedding in 3DGS, achieving this with minimal rendering interference and zero modifications to parameters or pipelines.
FedReview, a review-based mechanism to identify and dispose the potential poisoned updates in federated learning, is proposed, which enables the server to learn a well-performing global model in adversarial environments.
Tianhang Zheng, Yan-Lu Li, Bo-Han Deng et al.· 0 citations
This work proposes Defense-as-Skill, a defense paradigm that implements the runtime guard itself as an installable, inspectable, and editable skill, and demonstrates transfer across victim models, held-out risk families, and external benchmarks, as well as retained protection against adaptive attackers.
Xiao-Fan Yang, Ziqi Miao, Dian-Bo Sui et al.· 0 citations
Overall, safety failure is best understood as a disruption of a low-rank output-routing mechanism, and it is shown that LoRA and ASAM mitigate early collapse by suppressing output-side sharpness, but their protection weakens at larger fine-tuning scales.
Yi-Tong Guo, Xiaoyi Chen, Si-Yuan Zhang et al.· 0 citations
Reach audiences
Advertise in front of researchers, engineers, and readers.
This work identifies two complementary laundering regimes: OpenAI models produce the strongest payload disruption across the evaluated schemes, whereas Nano Banana 2 shows that DwtDct remains vulnerable under high-fidelity reconstruction.
MutMem V2 supports claims about portable integrity, authorization, traceability, conformance, and reproducibility under stated assumptions; it does not establish semantic truth, universal robustness, or independent replication.
This work shows that, despite disrupting the spatial structure required by conventional reconstruction attacks, transmitted token embeddings retain substantial positional information, and introduces the Spatially Aligned Reconstruction Attack (SARA), a unified pipeline that predicts token positions, restores their spat...
C-SafeQA, a policy-grounded benchmark for response-level Chinese safety evaluation, is introduced, with substantial trade-offs between unsafe-response recall and risk-query-conditioned safe-response false positive rate.
Rui-Wei-Chun-Hui-Li-Yuan Yang, Shuang Huang, Jun-Hua Liu et al.· 0 citations
Athea, the first graph-based approach for vulnerability affected library identification, is proposed, which models vulnerability databases as a knowledge graph and reformulates the identification problem as knowledge graph completion (KGC).
P. Duy, Trang Dang Yen, Hưng Nguyễn Hữu et al.· 0 citations
This work presents HiveTraceGuard-Pro, a 0.6B generative guardrail LoRA-tuned from Qwen3-0.6B that has the highest clean Russian robustness combined-F1 and Russian prompt-injection recall and releases the merged weights on Hugging Face under Apache-2.0.
N. Oblakov, Sabrina A. Sadiekh, Evgeniy Kokuykin· 0 citations
DP-TabImage is proposed, a modality-specialized framework for private paired synthesis that achieves a strong balance among tabular fidelity, image fidelity, and cross-modal alignment and reveals that visual warm-up primarily improves marginal image fidelity, whereas aligned table-image warm-up is critical for improvin...
Kai Chen, Josephine Lamp, S. Jha et al.· 0 citations
This work systematizes MAS security through an execution-centered analysis of 197 works, introducing an A-I-R framework that organizes attacks by adversary position, interaction interface, and resulting system-level risk, unifying otherwise fragmented attack mechanisms across MAS.
Rui Yang, Jun-Jie Xu, Zheng-Yu Liu et al.· 3 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.