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arXiv2026

Black Box Cryptanalysis of AES128

This paper presents computational results of cryptanalysis of AES using the Local Inversion by Black Box computations of the forward encryption and utilizes these results to develop a practically feasible approach for the key recovery of the full scale AES128 under Known Plaintext Attack (KPA). It is shown that complete recovery of unknown key bits is possible upto $80$ bits in a practically feasible time and memory in random KPA situation by sequential computation when remaining $48$ bits are known. The results of key recovery in $64$, $72$ and $80$ bit unknown cases are extrapolated to predict the period of the iterative sequence generated in the local inversion approach for the full $128$ bit unknown key case and a strategy is proposed to search the actual period by brute force parallel search of the sequence period with $10$ free bits defining the search space. Then it is shown that the actual key can be verified in polynomial time by fast powering of the forward encryption map. Hence this strategy shows that the key recovery problem for AES128 under KPA has a chance of success in practically feasible time for a majority of the plaintexts. Local inversion approach to cryptanalysis using black box computations is a universal method applicable to a vast variety of key recovery and map inversion problems. Hence the results presented in this paper are representative of estimates of cryptanalysis of other ciphers which can be considered almost as strong as AES128 as encryption functions.

cs.CR↗
arXiv2026

CARVY-FL: Client Anticlustering for Robust Voting in Provably Secure Federated Learning

Federated learning (FL) enables collaborative training without directly sharing raw data, but remains vulnerable to malicious clients. Voting-based FL improves robustness by partitioning clients into groups, training one model per group, and aggregating predictions by plurality voting. However, under class-disjoint non-IID data, distribution-oblivious grouping can yield highly variable certified accuracy (CA). We propose CARVY-FL, which estimates client distribution types from one-epoch model updates and uses anticlustering to increase within-group distributional diversity. Under a fixed grouping, CARVY-FL retains the voting-based CA guarantee while increasing vote margins. Experiments on MNIST and Fashion-MNIST show higher CA than FLCert. Under BadNets with model replacement, CARVY-FL improves the AUC of 100-ASR by 11.1% and 14.9%, respectively.

cs.CR↗
arXiv2026

A Comprehensive Survey on Linguistic Steganography: Methods, Countermeasures, Evaluation, and Challenges

Linguistic steganography hides secret messages in natural language text. Large language models (LLMs) have reshaped the field, but a systematic account of how these scattered advances collectively reshape the field in this new era is still missing. We provide one along four axes: 148 steganographic methods, 60 linguistic steganalysis countermeasures, 23 evaluation metrics, and 9 open challenges, each with taxonomies, reviews, and adoption analyses. Cutting across these axes, we identify five specific paradigm shifts in the LLM era: (1) from covertext modification to prompt-only generation, (2) from heuristic to provable security, (3) from white-box symmetric LMs to black-box or asymmetric access, (4) from security-centric designs to joint optimization, and (5) from text-quality concerns to engineering issues. The survey aims to serve as both a reference and a roadmap for practical and responsible linguistic steganography in the LLM era.

cs.CR↗
arXiv2026

UiAs: User-Independent 3D Facial Anti-Spoofing via Multi-modal Wireless Signals

Face authentication is widely deployed in security-sensitive applications, while increasingly realistic 3D spoofing attacks pose growing threats. High-fidelity 3D masks can reproduce facial appearance and geometry but cannot replicate the intrinsic physical responses of living tissue, which can be actively probed by wireless signals. However, the resulting liveness cues captured by wireless signals are entangled with user-dependent facial geometry, limiting cross-user generalization. We present UiAs, a multimodal user-independent 3D facial anti-spoofing system using electromagnetic (mmWave) and mechanical (acoustic) waves. The two modalities share similar user-dependent geometric variations, allowing UiAs to suppress them through cross-modal subtraction while preserving modality-specific liveness cues. Their complementary physical responses further improve live/spoof discrimination. In practical deployments, multiple materials (e.g., skin, hair, eyeglasses, or face coverings) may also bias liveness representations, while spoofing materials are diverse and open-ended. UiAs addresses both through skin-anchored contrastive learning. We evaluate UiAs with real 3D spoofing attacks, which achieves 93.25\% accuracy for unseen users without user-specific physical-signal enrollment.

cs.CR↗
arXiv2026

Auditing and Mitigating Privacy Leakage in Cloud-Edge Collaborative Decoding

Applications such as personalized assistance and proprietary document analysis require large language models (LLMs) to generate outputs from private data. Yet powerful LLMs typically cannot be deployed on the resource-constrained devices where private data resides, and uploading private data to cloud-hosted LLMs exposes sensitive information. Recent work addresses this tension with a cloud-edge collaborative decoding paradigm, where private data are kept on the edge with a small language model (SLM) producing next-token distributions, which are fused with predictions from a cloud LLM operating solely on public data. In this paper, we systematically analyze the privacy risks of such a paradigm with a novel evaluation framework using constructed QA datasets, which show that such collaboration can expose substantial private-context information. To address such privacy leakage, we propose CoVeil, a defense mechanism which dynamically optimizes transmitted signals to suppress leakage during decoding time while preserving the collaborative quality. Extensive evaluations demonstrate that CoVeil consistently improves the privacy-utility trade-off over existing baselines by reducing data leakage by up to 87.2%, with minimal accuracy loss.

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arXiv2026

Not the Same Protector: Deployment-Dependent Protective Intervention in LLMs

We ask whether a model protects a user in the same way when that user speaks rather than types. Using a single distress vignette---a physical injury of unstated severity following an interpersonal conflict---we present four frontier models with matched inputs across voice, text, and raw API deployment conditions (n=30 per cell) and code each response along five binary protective indicators, including whether the model issues an explicit medical-care directive. Voice-interface responses are markedly shorter than text-interface responses for three of the four models, and protective behavior contracts alongside that compression: medical directives are at ceiling under both the API and text conditions but decline under voice for every model tested. The contraction is not reducible to length. One model produces voice and text responses of comparable length yet still drops medical directives, and another falls below ceiling between its API and voice conditions, whose responses are of nearly identical length. Under raw API access the pattern is categorical rather than partial: no model asks after the user's safety even once. These results show that protective intervention is sensitive to the surface through which a request arrives, that this sensitivity is detectable using a simple protective coding scheme, and that it is not explained by turn length alone.

cs.CR↗
arXiv2026

WoE Wrote It? Watermarking Mixture-of-Experts LLMs for Black-Box Text Provenance

Large Language Model (LLM) watermarks provide a mechanism for text provenance, enabling model owners to identify machine-generated content and attribute it to a specific watermarked model. However, current LLM watermarking approaches predominantly rely on inference-time sampler methods and focus their analysis on dense models. Inference-time methods are only effective when the text is explicitly generated via the model owner's controlled API; they fail in a post-compromise scenario. An adversary who steals or leaks the model weights gains complete control over inference and can simply run an unmodified sampler, bypassing the watermark and preventing post-theft attribution. In this work, we introduce Watermarking of Experts (WoE), a novel black-box text provenance method that leverages the unique structural properties of sparse Mixture-of-Experts (MoE) models. WoE biases the vocabulary of specific experts and shifts the watermark signal embedding away from unenforceable inference wrappers. This approach ensures the watermark remains intrinsic to the model parameters, enabling defenders to attribute text generated by stolen weights, leaked checkpoints, and secondary dense models distilled from the stolen architecture without needing access to the adversary's deployment or weights. We evaluate WoE across eight MoE models, demonstrating successful watermark detection from suspect text, achieving an average true positive rate of 90.1% at a 1% false positive rate, reaching up to 94.9%, while largely preserving general model utility. Furthermore, WoE remains detectable under adversarial supervised fine-tuning, model extraction, and output-level paraphrasing, forcing malicious actors into a trade-off in which weakening the attribution signal requires additional model adaptation or text-rewriting operations, or compromises the utility of the resulting output.

cs.CR↗
arXiv2026

GhostSplat: Input-Triggered Backdoors for Multi-View-Consistent 3D Content Manipulation in Feed-Forward Gaussian Splatting

Feed-forward 3D Gaussian Splatting (3DGS) reconstructs a 3D scene from sparse images in one forward pass. Its shared pretrained weights also expose a supply-chain attack surface. Existing Neural Radiance Field and 3DGS backdoors modify individual scenes and activate at selected viewpoints; they do not install persistent behavior in shared generator weights. We introduce GhostSplat, an input-triggered backdoor that installs such behavior in feed-forward 3DGS. A low-amplitude pattern added to the input images causes the poisoned generator to render an attacker-chosen payload on unseen victim scenes. Anchoring the payload to a 3D point and reprojecting it into each target view makes the payload multi-view consistent. Exact projection onto the generator's representation-specific consistency set leaves a realized payload unchanged because the output already belongs to that set. The GhostSplat training framework succeeds across three architectures (MVSplat, pixelSplat, DepthSplat) and two datasets (RealEstate10K, ACID). Its strongest evaluated injection and deletion settings reach 96% and 100% ASR, respectively, with zero observed false positives while surviving JPEG, blur, and resampling. Defenses that use only that exact projection are therefore insufficient; effective mitigation requires information or intervention beyond same-set consistency projection.

cs.CR↗
arXiv2026

A Broadcast Authenticated Encryption with Keyword Search in the Standard Model: Tightly Secure in Multi-User, Multi-Challenge Settings

However, no known work considered the functionality requirement in its most realistic setting. We propose a new security definition of BAEKS in the multi-user (with adaptive corruptions) and multi-challenge (both in terms of ciphertext and trapdoor in an interleaved manner) settings. We also study the question of the unforgeability of BAEKS. In fact, our strong hiding requirement already implies a significant amount of unforgeability. We then propose a new BAEKS construction in the bilinear pairing groups. We prove this scheme achieves adaptive tight full-hiding security under (almost) standard MDDH assumptions. Restricting our BAEKS construction for a single receiver also gives an efficient and tightly secure PAEKS construction. We further run experiments to implement and evaluate our scheme.

cs.CR↗
arXiv2026

Asymmetric Phase Coding Video Watermarking

Existing video watermarking systems are symmetric: the party that can verify a mark holds the extractor weights or generator secret and can therefore also embed one. Benchmarks confirm the consequence, reporting that white-box forgery defeats all evaluated methods. We present a training-free video watermark that removes the shared secret. The signer embeds a complete Ed25519 signature into the phase spectrum of the chroma plane; any party holding the 32-byte public key and public per-video metadata verifies offline, with no model, no registry, and no network. The payload, 1024 bits of signed message with error correction, is an order of magnitude above common learned payloads and is carried by three design elements: a run-length temporal layout whose decoder identifies payload groups by correlation and never reads a frame index, a payload-free search that recovers scale, rotation, and translation from the carrier itself, and a closed-loop signing procedure that selects each video's embedding strength by self-verification through the unchanged public verifier. On 1000 uncurated real-world clips the system ships a verifying signature for 99.3% of the corpus and accepts a wrong public key zero times in 1000 attempts. An attack-aware acceptance gate yields embeddings that survive H.264 re-encoding at 100% and 50% rescaling at 97.4% on gated clips. The signature also verifies through a real display and capture loop, an axis absent from published evaluations.

cs.CR↗
arXiv2026

A Multi-Month Study of Git Commit Signing

Git commit signing, introduced in 2012, is one mechanism for establishing commit provenance in software supply chains, yet developer-controlled adoption remains rare and developers' experiences using it are understudied. To examine this experience, we conducted a three-month study with senior undergraduate and graduate computer science students (n = 22), whom we treat as proxies for junior developers. Participants configured commit signing independently, used it across four coursework projects, extended it to a second device, examined an external repository containing anomalous commits, and answered security-reasoning prompts. We found that while almost all participants successfully signed every commit and rated routine signing positively, many faced friction during setup, multi-device configuration, and repository verification. Despite signing all semester, they struggled to spot anomalous commits during verification, with over a quarter finding none. Additionally, nearly half expressed at least one misconception regarding signing guarantees or key management. Without isolating whether these difficulties stemmed from tooling, education, or understanding, we conclude that making signing easier is not enough to ensure effective security use. Rather, secure adoption also requires tools and education that support signature verification against authorized identities, interpretation of missing signatures and unknown keys, and correct reasoning about key-lifecycle operations.

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arXiv2026

Unlearning on Spatio-Temporal Graphs through Subgraph Virtual Edge Reconstruction

Spatio-temporal graphs are widely used in modeling complex dynamic processes such as temporal forecasting, molecular dynamics, and healthcare monitoring. Recently, stringent privacy regulations such as GDPR and CCPA have introduced significant new challenges for existing spatio-temporal graph models, requiring complete unlearning of unauthorized data. Since each node in a spatio-temporal graph diffuses information globally across both spatial and temporal dimensions, existing unlearning methods primarily designed for static graphs and localized data removal cannot efficiently erase a single node without incurring costs nearly equivalent to full model retraining. To address this, we propose CallosumNet, a spatio-temporal graph unlearning framework biologically inspired by the corpus callosum structure. CallosumNet makes two key technical contributions: (1) it reconstructs subgraphs using biologically-inspired virtual edges; and (2) it restores interlinked spatio-temporal dependencies among subgraphs via a lightweight meta-graph integration layer. Empirical results on four diverse real-world datasets show that CallosumNet achieves complete unlearning while maintaining accuracy very close to the gold model. The code is publicly available at https://github.com/wenlu-lab/STGraphUnlearning.

cs.LG↗
arXiv2026

Safe to Resume? Breaking Execution Continuity of Agent Execution via Rollback

AI agents are moving toward persistent, stateful execution across various applications, accumulating execution state and external effects that are costly to reconstruct after failures. Checkpoint and rollback (C/R) are becoming essential for recovery, yet their security implications remain largely unexplored. Correct rollback does not imply secure recovery: a faithfully restored checkpoint may resume an execution whose states, assumptions, and external effects never coexisted in any valid history. In this paper, we present the first systematic security study of checkpoint and rollback in existing agent systems. By examining representative agent C/R systems, we characterize the design space of existing C/R mechanisms and develop a general execution model that captures their recovery boundaries and state dependencies. From this model, we identify five fundamental failure modes spanning incomplete or inconsistent internal state, stale external dependencies, nondeterministic replay, and unrecorded external effects. We further demonstrate their security impact through three end-to-end attacks on Hermes, Cline, and LangGraph, enabling malware-verification bypass, unauthorized mail forwarding, and double payment. To systematically study these failures in practice, we develop a multi-agent analysis pipeline that reconstructs execution semantics, identifies violations of the five failure conditions, and validates them through actual rollback. Across five representative frameworks, our evaluation shows that these failures recur across heterogeneous C/R designs and stem from a common gap between the state restored by a checkpoint and the dependencies required for secure continuation.

cs.CR↗
arXiv2026

Benchmark Contamination: A Taxonomy Organized by Defeated Mitigation

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 detection, not the question a reporter faces at publication: given the mitigations already applied, which validity threats remain open? We introduce a taxonomy organized by the mitigation each type defeats -- direct, derivative, temporal, distributional, and acquired -- spanning training-time and evaluation-time leakage. Holding out a private test set closes the first alone. The fifth is acquired during the evaluation itself; because it is a property of one run, it must be recorded with the reported score rather than with the benchmark release. We operationalize it as a four-field disclosure protocol in which "unknown" is a valid entry, released under CC BY 4.0 with a JSON Schema, a validator, and worked examples. Two coders external to the design team applied a pre-registered instrument to 41 documents. Per-variable linear-weighted $κ$ runs from 0.00 to 0.35 (median 0.21) over 29 main-pass documents against a single-coder test-retest ceiling of 0.84, collapsing under the class skew the registration anticipated; pooling raises it to 0.46 through chance correction rather than better agreement. Two variables fall below the prevalence-robust threshold registered in advance: strata reporting and the acquired type introduced here. Disagreement concentrates on when a variable applies rather than on what a document states. Elicitation budgets are reported in 13% of documents, and no document addresses all five types. The contribution is the taxonomy, the score-side artifact that follows from it, and a pre-registered measurement of instrument reliability and current disclosure.

cs.CR↗
arXiv2026

PRISE: Privacy-pReserving Image Searchable Encryption Scheme for Intelligent Vehicle Systems

With the continuous advancement of intelligent vehicle technology, the image data generated by vehicles has become increasingly critical in various applications, including driver assistance, traffic monitoring, and safety warning systems. However, this growing reliance on image data also raises pressing concerns regarding its security and privacy protection. Searchable encryption technology, as an effective means to protect data security, shows significant potential for application in the field of intelligent vehicles. In this paper, we propose a novel Privacy-pReserving Image Searchable Encryption Scheme (PRISE) to address the security and privacy challenges associated with image data in intelligent vehicles. The PRISE scheme employs Multilinear Principal Component Analysis (MPCA) to extract data features and integrates symmetric encryption and matrix encryption techniques to ensure image privacy protection. To enhance search efficiency, we utilize a Mahalanobis distance-based fuzzy C-means (FCM) clustering method, which accelerates the search process on the cloud server. We conducted comprehensive experiments to evaluate our proposed scheme, and the results were consistent with our analytical findings, confirming the security of our approach. Comparative experiments with existing schemes demonstrated that our proposed method achieves higher accuracy while maintaining a comparable query time.

cs.CR↗
arXiv2026

On the Consistency and Performance of the Iterative Bayesian Update

In many situations, estimating the distribution of users' data concerning certain attributes is important. To facilitate this estimation while safeguarding users' privacy, the local privacy model is commonly employed, in which each user applies a local protection mechanism to release a noisy version of their original data to the data collector. The original distribution is then estimated using methods such as Matrix Inversion (INV), RAPPOR's estimator, and iterative Bayesian update (IBU). In this article, we experimentally demonstrate that IBU significantly outperforms the other methods when user data is protected through metric privacy mechanisms. We also explain the mathematical reason for the suboptimal performance of INV under those metric privacy mechanisms. Conversely, IBU exhibits performance similar to INV under typical mechanisms of local differential privacy, specifically the k-RR and RAPPOR. In addition, we investigate IBU's consistency, which is a crucial property as it means that the estimate converges to the true distribution as the number of data points increases. In the literature, IBU is claimed to be consistent, but there is no proof for this claim. We provide a formal proof of consistency leveraging the fact that IBU is a maximum likelihood estimator. Finally, we examine scenarios involving an infinite alphabet for sensitive data and propose a method allowing IBU to operate effectively in these situations.

cs.CR↗
arXiv2026

GSPR: Aligning LLM Safeguards as Generalizable Safety Policy Reasoners

As large language models (LLMs) are integrated into numerous applications, LLMs' safety becomes critical for both application developers and intended users. Currently, great efforts have been made to develop safety benchmarks with fine-grained taxonomies. However, these benchmarks' taxonomies are disparate with different safety policies. Thus, existing safeguards trained on these benchmarks are either coarse-grained to only distinguish between "safe'' and "unsafe,'' or constrained by the specified narrow risk taxonomies. To leverage these fine-grained safety policies across multiple safety taxonomies, we propose GSPR, a Generalizable Safety Policy Reasoner to identify unsafe inputs and outputs with violated safety taxonomies and concise explanations. Unlike prior safeguards which only cover a fixed set of risk factors, GSPR incentivizes its reasoning capability with varied safety taxonomies through reinforcement learning. Our GSPR can be trained across multiple safety benchmarks with distinct taxonomies and naturally exhibits powerful generalization ability. We conduct extensive experiments to show that GSPR significantly improves existing safety guardrails' reasoning capabilities for both safety and category prediction tasks. Moreover, GSPR also achieves the least inference token costs with explanations.

cs.CR↗
arXiv2026

GREAT: Generalizable Backdoor Attacks in RLHF via Emotion-Aware Trigger Synthesis

Recent work has shown that RLHF is highly susceptible to backdoor attacks. However, existing methods often rely on rare tokens or fixed triggers, limiting their impact in realistic scenarios. In this work, we develop GREAT, a novel framework for crafting natural distributional backdoors in RLHF. Specifically, GREAT targets harmful response generation for a vulnerable user subpopulation featured by semantically violent requests paired with emotionally angry triggers. At the core of our framework is a trigger identification pipeline that operates in the model's latent embedding space, leveraging dimensionality reduction and clustering techniques to identify representative triggers. To enable this, we introduce a hierarchical and diversity-driven prompting strategy to construct Erinyes, a high-quality dataset of over 5,000 angry triggers curated from GPT-4.1. Our experiments show that GREAT significantly outperforms baselines in attack generalization to unseen triggers, while preserving standard utility and maintaining stealth under defenses.

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