117 results for “NTIA”

TextSeal2 is a distortion-free LLM text watermark that merges two or more keys into one virtual key and couples it to fresh noise through AutoMark's Gaussian copula. Under speculative decoding it verifies keyed drafts with an extra exponential race, so output matches the target model and acceptance rate is unchanged. On three LLMs at a 10^-3 false-positive rate, it detects 8 to 22 points more 100-token answers than TextSeal.

arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🛡️ Security

TextSeal2 is a distortion-free watermark for LLM output that keeps its signal under speculative decoding. It merges two or more keys into one virtual key coupled to fresh noise through AutoMark's Gaussian copula, and verifies drafts with an extra exponential race. On three LLMs it detects 8 to 22 points more 100-token answers than TextSeal at a 10^-3 false-positive rate.

arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🧠 AI

Kolesnikov and Novoselov give block basis reduction (BKZ) algorithms for linear codes that need polynomial or quasi-polynomial numbers of calls to a minimum weight codeword oracle at fixed block size. The earlier Ghentiyala and Stephens-Davidowitz bounds are exponential. The paper adds a truncated BKZ and a DeepBKZ variant, with an implementation for block sizes 2 to 24 and approximation-factor estimates on random codes.

eprint.iacr.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🛡️ Security

MedCBR is a two-stage alignment framework for medical vision language models. Difficulty Weighted Boundary Preference Optimization (DBPO) trains on counterfactual preference pairs at the model's capability frontier. Entropy Gated Grounded Policy Optimization (EGPO) then applies distilled clinical rubrics according to prediction entropy. It reaches 61.2% average accuracy on six medical benchmarks, with the largest gains on differential diagnosis, and the two stages combine super-additively.

arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🧠 AI

This paper replicates Chen et al.'s study of LLM-based smart contract vulnerability detection, first on GPT-4 and then on Mixture-of-Experts, RNN-like and State Space Models. The exact replication confirms the high-recall, low-precision pattern on GPT-4. The differentiated replication finds the pattern does not transfer uniformly: GPT-4 and Gemma-3 behave alike despite a roughly 100x parameter gap, while two MoE models diverge.

arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026🧠 AI

AgentAnchor is a session-keyed behavioral watermark for attributing LLM agent trajectory logs to an owner. It derives per-step keys from a secret key, a fresh public session ID and semantic observation states, and embeds parity evidence through a KL-bounded exponential tilt. The paper formalizes learned cross-session forgery, and a trusted verifier checks disputed logs using wrong-key calibration and abstention.

arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026🛡️ Security

AgentAnchor is a session-keyed behavioral watermark that lets an agent owner prove ownership of a trajectory log. Each step key comes from a secret key, a fresh public session identifier and semantic states of recent observations. Parity evidence is embedded through a KL-bounded exponential tilt of action choices. A trusted verifier checks disputed logs without rerunning the agent, using wrong-key calibration and abstention. The paper also formulates a learned cross-session forgery threat.

arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026🧠 AI

AdaRoll-GRPO is a method that allocates GRPO rollout budgets per question by difficulty instead of uniformly. It frames allocation as a sequential decision problem and learns an allocation policy with a Bayes-adaptive MDP, computed offline so training adds no online optimization cost. Experiments on several reasoning datasets and LLM architectures show gains over standard GRPO at the same total rollout budget.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🧠 AI

DCKV is a differentiable codec for the prompt KV cache of hybrid language models, which keep a KV cache only in their few full-attention layers. Transform, quantizer and entropy models (conditioned on prompt tokens and already decoded neighbouring positions) are trained end to end against a frozen model. On Qwen3.5-2B it stores a token in over 55 times fewer bytes than bf16 with at most 1.8 points lost on three long-context benchmarks, and over 100 times fewer with at most 3.6 points lost.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🧠 AI

CTGREP is a compiler-based framework that applies differential analysis across the whole Clang/LLVM compilation chain to find where, how and under which configurations compiler optimizations break constant-time cryptographic code. Applied to seven libraries, three Clang versions, six targets and thousands of configurations, it led to 48 cases fixed with developers. It also identified seven new problematic LLVM passes and bypassed existing compiler-flag mitigations.

eprint.iacr.orgpaperAdded 27 Oct 2026, published 26 Oct 2026🛡️ Security

Subexponential Algorithms for LWE via Rejection Sampling on Superlattices is an ePrint paper by Ligeng Fan, Changpeng Shao, Hongbo Li and Dingkang Wang. It extends the Ducas, Engelberts and Loyer superlattice framework to give a provable classical search-LWE algorithm for prime q in [n^2, 2n^2], using m=4n samples and time and space exp(O(n/ln ln n)). A second variant runs in 2^((1/2+o(1))n/ln R) for general parameters. It resolves a gap left by Kirchner and Fouque.

eprint.iacr.orgpaperPublished and added 26 Oct 2026🛡️ Security

Continual Learning Exam (CLE) is a benchmark of 120 human-authored tasks, each a five-level ladder (600 levels) across 89 professional roles and 12 occupational domains, built by 50+ domain experts. Agents must pass a level to unlock the next, and an evaluation agent scores submissions against human rubrics. Across 17 harness-model configurations, feedback plus one revision adds a median 5.3 points; the best system clears 47.7% of levels and finishes all five levels in 23.3% of tasks.

arxiv.orgpaperAdded 27 Oct 2026, published 25 Oct 2026🧠 AI

VeilDNS is a DNS-native protocol that uses a post-quantum KEM and AEAD to encrypt DNS questions and answers and authenticate the resolver in one UDP exchange on port 53, preserving the DNS wire format. It has stateless and stateful modes plus optional forward secrecy from the second query. Against ML-DSA-authenticated DoT/DoH/DoQ it reports up to 3.5x higher cold-session throughput and 13.7x fewer bytes; Tamarin proves query confidentiality, response authentication and forward secrecy.

eprint.iacr.orgpaperAdded 26 Oct 2026, published 24 Oct 2026🛡️ Security

This paper studies demographic bias in LLM-emulated electorates, using three U.S. presidential elections and comparing one closed-source and one open-source LLM. Removing gender from the voter profile cuts the largest group error by 22-42%, and removing education cuts it by 64%. Removing self-reported race raises mean group error by over 40%. Repeated queries and fairness instructions do not reduce errors and can double them.

arxiv.orgpaperAdded 27 Oct 2026, published 23 Oct 2026🧠 AI

GordieHowe is a language, certifying compiler and zero-knowledge proof system for building anonymous credentials on standardized signatures (ECDSA, RSA, ML-DSA). It proves an ECDSA-signed mobile driving license presentation in 64.8 ms with a 276 KiB proof, 10.1x faster than Google's Longfellow, and an ML-DSA-44 signature in 87.3 ms with at least 116 bits of quantum security. Compiler output and security theorems are checked in Lean. It introduces series-parallel IOPs and extends the BCS transformation in the QROM.

eprint.iacr.orgpaperAdded 24 Oct 2026, published 23 Oct 2026🛡️ Security

DRIVE is a per-sample weighting framework for single-stage fine-tuning when the source pre-training data is unavailable. It uses one differentiable objective: Shapley additivity splits each sample's contribution into anti-forgetting and adaptation parts, a covariance-based diversity term reshapes them, and a sample-characteristic prior stabilizes them. The authors prove better stability-plasticity guarantees than existing weighting schemes and report better performance, faster convergence and more robustness.

arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026🧠 AI

This paper measures how hints such as "[Source] thinks the answer is (X)" shift LLM answers on MMLU questions, varying the source between a professor, a friend and a dog across Qwen, Gemma and SmolLM models. The shift fits an exponential tilt: a roughly constant mean change in the hinted answer's log-odds, with susceptibility largely shared across sources. The shift persists when the source is declared inaccurate or the prompt warns against trusting it. Synthetic experiments show how KL-regularized reward training could produce this pattern.

arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026🧠 AI

Thomas Decru's ePrint 2026/2595 proves a corrected version of radical isogeny formulae for every N≥4, odd and even, over fields of characteristic coprime to 2N. It settles the sign choice for even N so that 2-isogeny walks stay on the volcano surface. One radical step costs about 12.5N multiplications and one exponentiation, and the paper shows how to evaluate radical isogenies on points, so a CSIDH class group action needs no torsion point sampling.

eprint.iacr.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🛡️ Security

Efficient Laconic Oblivious Transfer with Malicious Security is an ePrint paper (2026/2593, ASIACRYPT 2026 revision) giving the first maliciously secure laconic OT that uses its primitives only as black boxes. It adds equivocal vector commitments and maliciously secure witness encryption to the commitment-plus-witness-encryption paradigm. Security is proven in the UC model against malicious senders and in the context model against malicious receivers. Instantiations use LWE and bilinear Diffie-Hellman assumptions in the standard model.

eprint.iacr.orgpaperAdded 24 Oct 2026, published 22 Oct 2026🛡️ Security

Monitorability Is Trainable proposes an unsupervised post-training objective that maximizes mutual information between a model's chain of thought and its output. On three model organisms with degraded CoT (from compression, monitor optimization, and synthetic-document generalization), it recovers most or all lost monitorability. On undegraded policies it improves CoT monitor and activation probe prediction of unsafe behavior and raises consequential reasoning.

arxiv.orgpaperAdded 23 Oct 2026, published 21 Oct 2026🧠 AI

SBFS-FT (symmetry-based few-shot fine-tuning) adapts the MatterSim 1M universal machine-learned interatomic potential with low-rank adaptation on a small set of compounds sharing one crystal symmetry. Tested on half-Heusler (space group 216), space group 225 and spinel (227) datasets, it cut lattice thermal conductivity error 2-3 times versus the base model. It matched per-compound fine-tuning while taking 5-7 times less compute for 100 compounds.

arxiv.orgpaperAdded 23 Oct 2026, published 21 Oct 2026🧠 AI

This ePrint paper gives the first exact bit-level models of modular multiplication z=(x·y) mod 2^n. One model is for monomial prediction under 3SDP-woU, the other for impossible differential search. Applied to the MSX lightweight cipher, it yields a 7-round integral distinguisher (2^96 data) for MSX-128, key recoveries on 8 and 9 of 18 rounds at about 2^228 operations, and 1,984 seven-round impossible differentials. Both models are implemented in CLAASP.

eprint.iacr.orgpaperAdded 24 Oct 2026, published 21 Oct 2026🛡️ Security

A Spectral Theory of Local-to-Global Leakage is an IACR ePrint paper (2026/2571) by Rei Ueno on additive secret sharing over finite abelian groups. It gives an exact leakage decomposition into local leakage magnitude and spectral alignment across shares. Two channels with identical local mutual information can have exponentially different global leakage. Automorphism-weighted sharing can cancel leakage perfectly with finitely many shares, which ordinary additive sharing cannot do.

eprint.iacr.orgpaperAdded 21 Oct 2026, published 20 Oct 2026🛡️ Security

TokenGate is a small drop-in component that adds end-to-end learnable token pruning to trained LLMs for long-context prefill. It uses token-gated attention, inspired by Mixture-of-Experts, to re-weight attention logits with token importance scores, which gets gradients through the non-differentiable top-K step. The authors report 1.4-11.3x faster time-to-first token and 1.5-3.4x lower peak memory with no accuracy loss, on Qwen3 and Qwen3.5.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🧠 AI

Cost-Coupled Gittins (CC-Gittins) is a multi-objective extension of cost-aware Gittins search that recommends Pareto-optimal model-to-role assignments for LLM agentic workflows. It exploits the coupling between evaluation and deployment cost, alternating between seeking higher quality and lower cost. On eight quality-cost matrices (two to five roles, up to 1,681 assignments), it recovers a substantial share of the true Pareto frontier at about 10% of exhaustive-evaluation cost, and most of it by 30%. Latency is tested as a third objective.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🧠 AI

SpanGuard is an indirect prompt injection (IPI) guardrail that splits detection into two steps. Lightweight unsupervised segmentation first locates candidate instruction blocks in tool outputs, then a small LLM judges whether each block is authorized by the user's request. The authors report a 0.32% attack success rate with negligible benign utility loss, and the guardrail still works with substantially smaller models.

arxiv.orgpaperAdded 21 Oct 2026, published 19 Oct 2026🛡️ Security

SEER-Repro is a benchmark of 20 published papers based on the SEER cancer registry, with the data needed to reproduce their findings. An LLM agent reproduces 77.4% of 2,190 atomic claims. A second agentic pipeline finds alternative analytical choices and reruns the analyses, flagging potentially consequential choices in 9.5% of fully reproduced findings for expert review.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🧠 AI

This paper shows that training-free relaxed (lossy) verification in speculative decoding causes length inflation: completions get longer, mostly through extended reasoning traces, which offsets gains in accepted tokens per round. Across twelve target-drafter pairs, relaxed rules accept 1.34x as many tokens per round but produce 1.54x longer completions. Rules that cut verifier rounds usually lose substantial accuracy, so the authors argue completion length and verifier rounds must be reported.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🧠 AI

DLite is a history-free speculative decoding framework for agentic LLM workloads. It reuses the target model's contextual hidden states and predicts locally without keeping the draft model's historical KV cache, which avoids the up to 80% acceptance-length drop seen with long, multi-turn contexts. A lightweight sequential head restores intra-block causal dependencies, and a two-stage training strategy is used. It reaches 2-6 acceptance length and beats DSpark by up to 70%.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🧠 AI

FedRAPP is a personalized aggregation framework for Source-Free Federated Test-Time Prompt Tuning (SF-FTTPT), where clients adapt CLIP soft prompts on unlabeled, non-IID test streams without source data. It combines reliability-aware client assessment, a server-side exponential moving-average prompt memory and similarity-guided peer aggregation. Across nine CLIP benchmark datasets and ImageNet variants it beats standard federated aggregation baselines while keeping prompt-only efficiency.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🧠 AI

SpikeQ is a spike-guided post-training quantization framework for spiking neural networks on neuromorphic chips. It picks each weight level by simulating downstream neurons to minimize spike errors, and uses a reset-aware grid for pre-fire membrane potentials. With 4-bit weights and potentials, models stay within 2.59 points of full precision, with 7.8 to 7.9 times smaller memory and 6.4 to 6.9 times less energy.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🧠 AI

The paper proposes a traceable and aggregatable tagged witness signature (TA-TWS) and builds SwiftAuth on it, a conditional privacy-preserving anonymous authentication framework for UAVs and other dynamic terminals. Issuers can create authorization components while users are offline. Several credentials compress into one constant-size proof, verifiers can batch-verify across users, and a regulatory trapdoor lets authorities trace misbehaving users. TA-TWS is proven unforgeable and witness-hiding, and benchmarks with the MIRACL Core library on constrained devices and Aliyun servers show it is practical.

eprint.iacr.orgpaperAdded 17 Oct 2026, published 16 Oct 2026🛡️ Security

Studies "filesystem capability laundering" in MCP servers: client approval of a tool call grants the server process broad host filesystem access, so attacker-steered arguments can reach sensitive files outside the tool's declared contract. The authors build MCP-FSD, which infers per-tool least-privilege boundaries, plants credential fixtures outside them, and tests exploitability and sandbox containment by paired dynamic execution. Of 21,097 servers from nine registries, 16.4% of 4,806 triaged servers have filesystem capabilities despite non-filesystem classifications. Dynamic validation confirmed 150 vulnerable instances, and disclosure produced 13 advisories, including two CVEs in Anthropic's Git MCP Server.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🛡️ Security

Proposes protecting the retrieval datastore in RAG systems by applying the same random projection to datastore embeddings and query embeddings. This provably preserves query-item semantic similarity while substantially mitigating data extraction attacks. Across multiple RAG architectures and datasets it beats prior differential-privacy-based methods on retrieval and generation quality, with comparable empirical privacy under strong attacks, making it a practical baseline for privacy-preserving RAG.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

Proposes applying the same random projection to both datastore embeddings and query embeddings in RAG systems. The authors claim this provably preserves query-to-item semantic similarity while substantially mitigating datastore data-extraction attacks. Across several RAG architectures and datasets it reportedly beats prior differential-privacy-based defenses on retrieval and generation quality, with comparable empirical privacy under strong attack models. It is presented as a lightweight baseline for privacy-preserving RAG.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🛡️ Security

The paper fine-tunes a pretrained CNN wind-field super-resolution model with differentiable topology-inspired auxiliary losses on speed, gradient magnitude, soft high-speed level sets and local maxima. It also uses fixed-pair supervision at ground-truth persistence-pair endpoints located offline with the Topology ToolKit. A 16-configuration loss study on a fixed 168-field benchmark finds that gradient supervision gives the strongest persistence-diagram improvement, while fixed-pair supervision gives the most consistent joint improvement in persistence-diagram and merge-tree distances. The combined objective cuts bottleneck, W2,∞ and W2,2 distances by 31%, 25% and 27% relative to the pretrained CNN, at a cost of about half of standard fine-tuning's PSNR gain. It also shows that pointwise metrics and topological distances rank CNN and GAN models differently.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

T-EVOLVE extends the lattice-commitment e-voting protocol EVOLVE to threshold tallying. Votes are Shamir-shared, so any t of n authorities can finish the tally. Privacy holds against t-1 colluding authorities and verifiability holds even if all of them collude. The voter proves her committed shares form a Reed-Solomon codeword whose value at zero is a valid vote. Complaints about ciphertexts that do not decrypt are publicly verifiable, using an LWE-instantiable encryption scheme with publicly verifiable opening. Privacy, verifiability, individual verifiability and robustness are proven in the classical random oracle model, and the verifiability reduction is not tight. In a C implementation, a yes/no ballot for four authorities takes 109 KB, and an authority aggregates 10^4 ballots in about three minutes on one core.

eprint.iacr.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🛡️ Security

Harness^2 is a plug-and-play framework that recursively improves LLM agent execution harnesses in open-world settings where no ground-truth verifiers exist. Task-level recursion uses a contrastive Propose-Probe-Compose step to guide harness edits for the current task, and it scales through parallel or sequential recursion. Harnessing-level recursion distills experience from those edits into a continual-improvement harness for future tasks. A single step improves GLM-5 and Gemini 3.5 Flash over their base harnesses by up to 11.5 points on WorkBuddy-Bench, 4.5 on JobBench and 11.8 percentage points on LAB.

huggingface.copaperAdded 14 Oct 2026🧠 AI

Presents ChainGraph, a pattern that externalizes exploit-chain reasoning for LLM pentest agents (Claude Code, Codex) into a Neo4j property graph exposed via the Model Context Protocol. Findings, attack steps, credentials, assets and tamper-evident evidence receipts are typed nodes with pre/post-condition edges, queried with Cypher and Graph Data Science for next-step inference, entry-to-crown-jewel pathing, chain-severity promotion, provenance and chokepoints. On a synthetic GOAD-style multi-forest Active Directory engagement, three Medium findings become Critical once chained, betweenness centrality identifies break-points, and reasoning payload drops ~98% (token proxy); results are single-run and illustrative.

arxiv.orgpaperAdded 17 Oct 2026, published 14 Oct 2026🛡️ Security

ChainGraph is a pattern that moves exploit-chain reasoning out of the flat context window and into a Neo4j property graph. Findings, attack steps, credentials, assets and tamper-evident evidence receipts are typed nodes with pre/post-condition edges. The graph is wired to Claude Code or Codex through MCP with no custom harness. Five operator questions (next-step inference, entry-to-crown-jewel pathing, combined-severity promotion, evidence provenance, chokepoint identification) become reusable Cypher and Graph Data Science queries, plus a chain-severity score. On a synthetic multi-forest Active Directory lab modeled on GOAD, three findings rated Medium in isolation become Critical once their path to the forest root is materialized. Scoping each decision to a graph result cut the reasoning payload by about 98% on a token proxy. The authors say the results are illustrative single-run measurements, not a benchmark.

arxiv.orgpaperAdded 17 Oct 2026, published 14 Oct 2026🧠 AI

Presents a modular device-binding protocol for post-quantum anonymous credentials. The phone Secure Element signs a verifier challenge with an unmodified FIPS 204 ML-DSA interface, and the user proves knowledge of that signature in zero knowledge at each presentation. Using ML-DSA's structure avoids the cost of generic NIZK systems such as Longfellow. The most efficient variants assume weak-MLWR for unlinkability, and that assumption can be dropped at some efficiency cost. The protocol works with different anonymous credential schemes, and a proof-of-concept implementation is included. This matters for planned EU age-verification and Google Wallet/mDL rollouts, which need protection against credential sharing.

eprint.iacr.orgpaperPublished and added 14 Oct 2026🛡️ Security

Paper that links linear, integral, differential-linear and restricted differential distinguishers through two-way conversions with explicit divisibility guarantees and data complexity. It gives automated MILP/SAT models (Walsh and ultrametric representations, exact mix-basis models) and a black-box FWHT histogram search for zeros and finite divisibility. It improves Beyne–Verbauwhede divisibility bounds in 674 SIMON cases (e.g. 2^5 to 2^11 on 9-round SIMON32) and PRESENT. It also gives first higher-divisibility proofs for 8-round SPECK and BEANIE, new bounds for GIFT-64 and Ascon, and new properties on RC5, ChiLow-32, KeeLoq and KATAN32.

eprint.iacr.orgpaperPublished and added 14 Oct 2026🛡️ Security

DPF-SGD is a federated SGD algorithm that stays robust to Byzantine agents while satisfying ϵ-differential privacy. It combines private step sizes with a DP noise-decay schedule and adds a privacy-preserving comparative elimination (PPCE) filter that suppresses Byzantine updates during aggregation. The authors prove almost-sure convergence to the global optimum despite persistent noise and adversaries. On synthetic and MNIST data, final optimization error is one to two orders of magnitude lower than DP baselines.

eprint.iacr.orgpaperPublished and added 14 Oct 2026🧠 AI

Game2Real-Bench tests whether vision-language model performance on procedurally generated toy games predicts real-world visual reasoning, and whether game examples help transfer. It has 1,000 verifiable game samples and 400 real-world samples, matched by shared reasoning requirements (fine-grained perception, spatial reasoning, constraint reasoning, sequential planning). Across 25 VLMs, game and real scores correlate strongly (Pearson/Spearman 0.935). One-shot game-to-real transfer on four models improves constraint reasoning for several but hurts fine-grained perception. The authors also release Game2Real-Dataset, with about 24K verified game samples across 18 tasks and 65 QA families.

arxiv.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🧠 AI

The paper introduces verifiable reconstructible straightline codes (VRSCs) as an abstraction for compiling zkSNARKs into Universally Composable (UC) secure protocols. It shows that the witness-succinct UC compiler of Ganesh et al. (EUROCRYPT 2023) is missing a security requirement: permuting previously revealed evaluation openings yields a fresh accepting proof, which invalidates the claimed simulation argument. The fix uses verifier-enforced disjoint evaluation domains and a pairing-based Fischlin-style instantiation with structured Reed-Solomon codes and jointly computed KZG openings. This brings certificate generation to O(n log n) with constant extra proof size and verification work.

eprint.iacr.orgpaperAdded 14 Oct 2026, published 12 Oct 2026🛡️ Security

Mori and Emura give a generic transformation, a variant of the CHK transformation, that upgrades a public-key authenticated encryption with keyword search (PAEKS) scheme to one secure against chosen-ciphertext and trapdoor attacks (CCTA). It works because the PAEKS receiver names the sender when generating a trapdoor, so keywords can serve as tags. Applied to Li-Boyen generic PAEKS instantiations, it yields the first fully CCTA-secure PAEKS schemes from pairings and from lattices. Verification keys embedded in ciphertexts and trapdoors also let sender-receiver matching use hash tables, so search cost no longer depends on the number of senders.

eprint.iacr.orgpaperAdded 14 Oct 2026, published 11 Oct 2026🛡️ Security

The paper proposes a generic encrypt-then-MAC transformation for building CCA-secure KEMs. The PKE ciphertext is authenticated by a MAC keyed from the PKE plaintext, so decapsulation no longer needs the Fujisaki-Okamoto re-encryption step. It proves IND-CCA security in the random oracle model from OW-PCA, and IND-qCCA security from a new OW-qPCA notion. It presents ML-KEM-EtM and McElieceEtM (Classic McEliece), and benchmarks ML-KEM-EtM with Poly1305, GMAC, CMAC and KMAC256. The result is a substantially lower decapsulation cycle count for a small cost in encapsulation cycles and ciphertext size.

eprint.iacr.orgpaperAdded 14 Oct 2026, published 11 Oct 2026🛡️ Security

Introduces Policy-Constrained Diffusion Agents (CoDA), which enforces task-dependent security policies inside the denoising process of diffusion language models. At each step, token distributions are projected onto a policy-compliant set, so constraints hold throughout generation instead of relying on detecting injected instructions. Evaluated across several dLLM families under static and adaptive prompt-injection attacks, it reports substantially lower attack success rates with competitive task utility and practical inference overhead.

arxiv.orgpaperAdded 15 Oct 2026, published 10 Oct 2026🛡️ Security

ePrint paper that defines a security model for censorship resistance in anonymous credential systems, where a malicious issuer uses information gathered at issuance to selectively deny credentials. It gives a round-optimal construction built on blind signatures with selective disclosure at presentation. It also presents CRACS, an instantiation for the European Digital Identity Wallet (EUDIW). CRACS keeps selective disclosure and adds unlinkability between issuance and presentation. It confines the advanced cryptography to issuance, so secure elements, credential formats, signature schemes and verifiers stay unchanged and deployment can be incremental.

eprint.iacr.orgpaperAdded 14 Oct 2026, published 10 Oct 2026🛡️ Security

Introduces Confidential Error-correcting Codes (CECs), a primitive that decodes reliably from perturbed codewords while keeping the message confidential, defined by confidentiality, robustness and soundness. The Randomness-Security Separation framework encodes a nonce and masks the payload with a PRF; the Conc-CEC instantiation uses concatenated Reed-Muller and Reed-Solomon codes and claims stronger concrete security than PRCs (CRYPTO'24). The application, CECMark, watermarks AI-generated images and reportedly beats Gaussian Shading, PRCMark and T2SMark across 56 perturbations with a false positive rate below 1e-5 and no side effects.

eprint.iacr.orgpaperAdded 14 Oct 2026, published 10 Oct 2026🧠 AI

Introduces Confidential Error-correcting Codes (CECs), a cryptographic primitive for decoding perturbed codewords while keeping the message confidential, defined by confidentiality, robustness and soundness. The Randomness-Security Separation framework encodes a nonce and masks the payload with a PRF. The Conc-CEC instantiation uses concatenated Reed-Muller and Reed-Solomon codes, and the authors claim stronger concrete security than PRCs (CRYPTO'24). The CECMark application watermarks AI-generated images, and the authors report it beats Gaussian Shading, PRCMark and T2SMark across 56 perturbations with a false positive rate below 10^-5.

eprint.iacr.orgpaperAdded 14 Oct 2026, published 10 Oct 2026🛡️ Security

Presents an EM side-channel attack on LLaMA-3.2-1B-Instruct running on a Raspberry Pi 5, showing passive physical measurements leak generated tokens and user prompts. Three attacks: a profiling attack (97.2% accuracy over 50 medical/privacy keywords), a hidden-state reconstruction attack predicting tokens from the 128k vocabulary (94.3% on profiled classes; 67% of unseen words ranked in top 100), and a hidden-state inversion attack reconstructing prompts semantically. Shows that local edge inference does not guarantee confidentiality of tokens and inputs.

arxiv.orgpaperAdded 16 Oct 2026, published 9 Oct 2026🛡️ Security

Cryptanalysis of the Alternating Sponge (ASP) and Duet, a permutation-based hash candidate in China's ICCS Next Generation Commercial Cryptographic Algorithm program (NGCC). The authors give an O(2^r)-query distinguisher against ASP that contradicts its claimed indifferentiability bound in some parameter regimes. They also give a distinguisher and state-recovery attack on Duet's multi-block output, using O(2^(r+c-h)) permutation queries. Collision and preimage resistance are unaffected, but Duet used as a XOF or KDF is affected, and state recovery enables length extension.

eprint.iacr.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🛡️ Security

A narrative review in HCI arguing that the single-voice chatbot interface causes three harms: cognitive offloading, ideological narrowing of user beliefs, and epistemic compression of cultural and value diversity. It proposes multi-persona interfaces, which show several personas to the user at once, as a low-cost fix. The design draws on game design and participatory simulation (magic circle, role differentiation, persona embodiment). It offers a taxonomy for designing such interfaces and calls for empirical studies that link technical pluralistic alignment methods to real human-AI interaction.

arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🧠 AI

ePrint preprint extending the Ducas–Engelberts–Loyer provable subexponential Gaussian sampler (built on Wagner's generalized birthday algorithm) to module matrices over power-of-two cyclotomic rings. It gives provable 2^{O(n/lnln n)}-time algorithms for Module-SIS and for decision and search Module-LWE using only the k+ℓ ring samples provided, plus sample-preserving scalar search-LWE and narrow Module-LWE variants. Separate heuristic estimates suggest these Wagner-style attacks do not reach the NIST security targets of ML-KEM and ML-DSA.

eprint.iacr.orgpaperAdded 12 Oct 2026, published 8 Oct 2026🛡️ Security

ePrint paper giving four constructions of optimal threshold implementations (t+1 shares, no fresh randomness) for bijective S-boxes, a masking countermeasure against differential power analysis and glitch leakage in hardware. It shows that Q30^5 admits optimal TIs, gives a matrix criterion for quadratic permutations (yielding new 6-bit classes), and gives a weighted interpolation construction for power permutations that disproves the Piccione et al. 2023 conjecture. It also gives a derivative-uniform framework that proves every 4-bit cubic permutation admits optimal TIs. A companion repo has TIs for 5-bit quadratic permutations, 4-bit cubic permutations, the PRESENT S-box and GF(16) inversion.

eprint.iacr.orgpaperAdded 11 Oct 2026, published 8 Oct 2026🛡️ Security

This paper studies where tool-using LLM agents break down in the chain from gathering evidence to taking consequential actions, finding that strong static judgment of whether to act often coexists with much weaker interactive execution, and that failures arise from incomplete investigation, acting before evidence is established, or unresolved prerequisites in multi-action workflows. The authors introduce SafeActBench, a 656-case benchmark across six operational domains and five protocols, paired with a provenance-bound Evidence Ledger and a deterministic trajectory evaluator tested across ten model-harness configurations.

arxiv.orgpaperAdded 7 Oct 2026🧠 AI

This IACR ePrint paper by Yi-Fu Lai and Yu Yu breaks the blindness property of a lattice-based blind signature scheme presented at Crypto 2020, which uses a linear hash function framework instantiated over lattices. The authors first identify an overlooked bug in the scheme's original blindness security proof, then construct a concrete attack under an honest-key, honest-but-curious signer model by exploiting correlations between leaves of the scheme's tree structure, and discuss how the construction can be repaired.

eprint.iacr.orgpaperAdded 8 Oct 2026, published 7 Oct 2026🛡️ Security

RECAST (Routing Evidence through Computation, Access, and Synthesized Tools) reframes retrieval-augmented generation as a sequential decision process: a lightweight RouterLM iteratively selects or formulates primitive/custom operations, a frozen CompilerLM turns custom operations into executable code, and a frozen AnswerLM produces the final answer once evidence is judged sufficient. RouterLM is trained with supervised fine-tuning followed by GRPO, and the system reaches a 75.6% mean success rate across six heterogeneous benchmark families, beating the strongest large-model baseline by 15.9% and a training-free Gemini 3.5 Flash router by 5.0%, with strong zero-shot generalization on three held-out benchmarks. It targets tasks where evidence must be derived through filtering, aggregation, or computation across multiple sources rather than retrieved verbatim.

arxiv.orgpaperAdded 8 Oct 2026, published 7 Oct 2026🧠 AI

This arXiv paper shows that large language models can be trained to tolerate unreliable, faulty digital hardware, and that their error resilience improves rather than degrades as model size increases. Using modified neural scaling laws derived from 40,000 GPU-hours of training runs on simulated faulty hardware, the authors argue that LLMs learn to compute within "good" error-correcting codes whose relative overhead stays finite regardless of scale, suggesting a path toward running AI inference on low-energy, unreliable hardware for substantial energy savings.

arxiv.orgpaperAdded 8 Oct 2026, published 7 Oct 2026🧠 AI