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CategoryAll Vulnerability 14 Exploit / PoC 13 Advisory 8 AI safety & security 7 Industry 7 AI industry 6 Threat actor 6 Data breach 5 Policy & legal 3 Research 3 Ransomware 2 Malware 2 AI research 2 Model release 1

SeverityAll critical 11 high 27 medium 9 low 2 info 31

Kubernetes Attack Lets Hackers Steal SPIFFE Workload Identities and Impersonate Applications

Unit 42 detailed a Kubernetes technique where node-root attackers spoof cgroup selectors to steal SPIFFE/SPIRE workload identities and impersonate applications.

Palo Alto Networks Unit 42 described a post-exploitation technique in which an attacker with root access to a Kubernetes node manipulates cgroup metadata so the local SPIRE agent issues valid SVIDs belonging to co-located workloads. Stolen X.509 or JWT SVIDs let the attacker impersonate victim applications over mutual TLS or pass identity-aware authorization, turning node compromise into lateral movement and privilege escalation. Unit 42 said it has not observed exploitation in the wild and released the open-source Spooffe tool so defenders can measure which identities are harvestable per node.

GBHackersupdated 11:02 UTC · 2h agofirst 10:36 UTC · 2h agoResearch 2 sources

Locus: A Framework for Exploring and Optimizing Point Addition Hardware for Zero-Knowledge Proofs

Locus framework automates ASIC and FPGA point-addition designs for elliptic curves, achieving 2.71x speedups and 3.11x area reductions for ZKPs.

Locus is a framework that automatically generates ASIC and FPGA implementations of elliptic curve point addition (PADD) for supported equation forms, enabling exploration of over 1,000 design points. On a 12nm technology node, its designs achieve a 2.71x geomean speedup and 3.11x geomean area reduction versus prior ASICs, plus 34.67x geomean speedup over CPU. Integrated into a prior ZKP accelerator at iso-area, it yields a 3.15x geomean speedup on end-to-end proof generation. The framework is open source on GitHub.

arXiv cs.CRSep 16, 15:53 UTC · 21h agoResearch1

s-MDM: Generative Virtualization of Multi-Device Hardware Variations for Portable DL-SCA

Researchers present s-MDM, a generative framework synthesizing virtual device profiles to improve cross-device portability of deep learning side-channel analysis.

The poster introduces the Synthetic Multiple Device Model (s-MDM), a zero-target-trace generative framework addressing performance degradation of deep learning side-channel analysis on unseen hardware. It combines a structured cVAE generator, Walsh-Hadamard leakage anchors, continuous style modulation, and decoupled leakage-style-domain critics to synthesize virtual source-device profiles offline. Benchmarked on 32-bit AES_PTv2 traces, s-MDM achieves consistently low key rank on layout- and acquisition-shifted Pinata targets where physical baselines are unstable.

arXiv cs.CRSep 16, 15:04 UTC · 22h agoResearch