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Shuffling is Not Enough: Breaking Permutation-Based Model Confidentiality in Hybrid FHE Inference
Attack breaks permutation-based model confidentiality in hybrid FHE inference, recovering all ResNet-20 linear layers exactly with d+1 queries per layer.
The paper shows output-permutation plus noise fails to protect model confidentiality in hybrid FHE inference: d+1 admissible queries recover an exact permutation-invariant summary of a d-input linear layer, and shuffle-model DP amplification premises cannot hold under correctness-bounded noise. The authors recovered all linear layers of a Safhire-style ResNet-20 end-to-end from TFHE transcripts with zero error, using 5,712 total queries. Exact per-layer recovery was also confirmed on pretrained ImageNet-scale CNNs and ViT-B/16. Leaked layer spectra enable model fingerprinting, lineage attribution, and improved logit-based extraction, while suppressing them destroys inference utility.
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Proximity Gaps for Gabidulin Codes and Applications
Researchers prove proximity-gap bounds for rank-metric and Gabidulin codes, enabling the first polynomial commitment scheme framework based on rank-metric error-correcting codes.
The paper proves every linear rank-metric code admits a proximity gap for deltas up to (d-1)/(3n) with error at most q^(e+1)/q^m, and improves the gap to (d-1)/(2n) for Gabidulin codes with error at most 10q^(n-1)/q^m, matching bounds for Reed-Solomon codes. A constructed infinite family of constant-rate Gabidulin codes shows the (d-1)/(2n) bound is tight, and a counterexample establishes a lower bound on the error at the d/(3n) gap. Applications include an IOPP for interleaved Gabidulin codes adapted from the Ligero IOPP and a q-linearized polynomial commitment scheme adapted from Ligero-based PCS, reportedly the first PCS framework based on rank-metric codes.
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An Open-Source End-to-End FHE Implementation for Privacy-Preserving Llama 3 8B Inference
Odin runs Llama-3-8B fully homomorphic encrypted inference on a single H100 in 366 seconds, a 4.51x speedup over THOR.
Odin is an open-source end-to-end GPU CKKS implementation for privacy-preserving Llama-3-8B inference that co-designs ciphertext packing with model execution. A feature-major cross-layer layout unifies residual connections and layer interfaces, while transient intra-operator layouts serve linear projections and attention, avoiding intermediate repacking of QK^T softmax outputs. Minimax polynomial approximation with input-range control reduces polynomial degree and multiplicative depth for nonlinear ops. With 128-token input, Odin evaluates all 32 Transformer layers on one NVIDIA H100 80 GB in 366.4 s using 58.9 GiB peak memory, versus 1651.9 s for the THOR baseline, a 4.51x speedup.
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Machine Unlearning as Private Retroactive Algorithms
A cs.CR paper defines private retroactive algorithms, showing machine unlearning is a data-maintenance problem and giving DP constructions for linear statistics, clustering, histograms.
The paper argues that machine unlearning's requirement to emulate retraining from scratch carries no meaningful privacy semantics against adversaries observing sequences of releases, recasting it as a data-maintenance question addressed by retroactive algorithms. It defines private retroactive algorithms, combining retroactivity with differential privacy under continual observation. Constructions achieve privacy and retroactivity at no asymptotic cost over privacy alone for linear statistics, clustering, and histograms, alongside impossibility results.
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IoT Under Siege: The Anatomy of the Latest Mirai Campaign Leveraging Multiple IoT Exploits
Unit 42 tracks a Mirai botnet campaign exploiting over 20 IoT vulnerabilities in routers, cameras and DVRs to build DDoS botnets since March 2023.
Since March 2023, Unit 42 has tracked threat actors exploiting more than 20 IoT vulnerabilities to spread a Mirai botnet variant, first seen downloading payloads from zvub.us on March 14, 2023. Exploited flaws span CVE-2023-1389 (TP-Link Archer), CVE-2022-30525 (Zyxel), CVE-2022-31499 (Nortek) and many router, camera and DVR bugs. The variant decrypts configuration strings with an XOR key derived from 0xDEADBEEF and lacks built-in credential brute forcing, so spreading relies on manual operator exploitation. Two campaigns observed since October 2022 share infrastructure and near-identical samples.
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GAUGE: A Formal Framework for Measuring Cryptographic Security under Heterogeneous Adversary Cost Models
GAUGE frames cryptographic security as profiles over adversary cost models, certifying a ranking reversal between ML-KEM-512 and AES-128 from a 4–5% memory pricing shift.
GAUGE represents cryptographic security as a function over admissible adversary cost models (a security profile), proves profiles are piecewise-linear and concave, and establishes a rating trilemma when two profiles cross. A polynomial-time linear-programming procedure certifies whether the ranking of two schemes is robust, reverses under admissible models, or is genuinely incomparable. Applied to NIST post-quantum standards, the framework certifies a ML-KEM-512 versus AES-128 ranking reversal from a 4–5% shift in memory pricing and measures lattice-sieving cost drift of 9.79 bits per year over eight years. A hybrid X25519 + ML-KEM-768 handshake reduces combined-break probability twenty-fold at a 2.3 kilobyte cost.
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Hunting Vulnerabilities Using Frontier Models
Okta used frontier AI models GPT-5.5 Cyber and Mythos via OpenAI and Anthropic programs to scan millions of code lines for vulnerabilities.
Okta describes using frontier AI models, including GPT-5.5 Cyber Preview (TAC) and Mythos Preview, through OpenAI's Daybreak Cyber Partner Program and Anthropic's Project Glasswing to hunt vulnerabilities across its product codebase. The team built a custom Python orchestrator with strong isolation, vendor-agnostic model support, and four distinct scanning pipelines executed as isolated Codex or Claude Code sessions with progressive context loading to reduce context bloat. Human experts and AI agents worked both autonomously and in paired hunts, and Okta reports the best results when humans and agents taught each other.
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CISA Alerts on Active Exploitation of Flaws in Fortinet, Ivanti, and Nice Products
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Subgroup Packing for Batched PASTA Transciphering
Interleaved subgroup packing cuts PASTA homomorphic transciphering server cost by a median 1.60x at the expense of remaining noise capacity.
The paper studies how record word layout affects the cost of PASTA transciphering, where a server converts symmetrically encrypted records into homomorphic ciphertexts. A subgroup layout interleaves records so cyclic shifts preserve positions, reducing required displacements from 255 to 128 versus contiguous packing. In HElib across twelve paired corpora under six homomorphic keys, the median direct-to-subgroup server cost ratio is 1.60 including fresh generation, conversion, and two queries, but with less remaining noise capacity.
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OpenAI Builds ‘Defense Factory’ Where AI Agents Continuously Find and Fix Vulnerabilities
OpenAI unveils a Defense Factory where AI agents continuously discover, validate, and fix vulnerabilities, integrating GitHub, Snyk, Semgrep, Tenable, and ServiceNow.
OpenAI introduced a Defense Factory, an agent-first cybersecurity operation that connects AI agents to developer and security tools via APIs, CLIs, and Model Context Protocol integrations including GitHub, GitLab, Snyk, Semgrep, Tenable, Jira, Linear, and ServiceNow. During an internal security sprint, over 250 people across more than 100 service areas closed 53 urgent or high-priority issues on day one, achieved a 90.6% accepted ownership-assignment rate, and Codex generated all remediation patches with only 0.53% rolled back. Agent-assisted deduplication flagged 37% of findings as duplicates, and runtime validation reproduced 19.5% of findings, cutting the false-positive rate to 0.81%. OpenAI argues defenders must exploit a temporary 'defender's window' using source-code access and frontier models before open-weight models enable autonomous offensive agent fleets.
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