Today's cyber intelligence queue

Attackers Steal METR API Key and Consume AI Credits Worth About $600,000

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WatchGuard Patches Critical Vulnerabilities

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Russia-Aligned UAC-0099 Plants Nuclear Weapon Prompt in Malware to Disrupt AI Analysis

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Attackers Exploit Critical Langflow and Rails Flaws in Credential-Probing and C2 Activity

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PaperCut Exploitation Escalates to Active Intrusions

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HSMLog: Small Language Model-Assisted Hardware Security Module Log Anomaly Detection with Behavioral Analysis

arXiv:2608.29773v1 Announce Type: new Abstract: Hardware Security Module (HSM) logs capture security-critical behavior, but anomalies emerge from relationships across event sequences, keys, object states, sessions, and temporal patterns rather than isolated events. Existing methods separate detection from HSM-specific evidence validation and reporting. In this paper, we present HSMLog, a two-stage framework for HSM log anomaly detection with retrieval-grounded behavioral anal…

Building the Truman Show: A TrustZone-Based Framework for Lightweight Out-of-band Kernel Security Monitoring

arXiv:2608.29758v1 Announce Type: new Abstract: The increasing number of vulnerabilities in operating systems, together with sophisticated kernel-level threats (e.g., rootkits), has weakened the effectiveness of traditional in-kernel protection mechanisms. Since these defenses operate at the same privilege level as the kernel, they share the same attack surface and can be bypassed once the kernel is compromised. Isolation-based security approaches provide stronger protection…

JITterFlip: Uncovering Fault Attack Surfaces in JIT-Compiled LLM Serving

arXiv:2608.29745v1 Announce Type: new Abstract: LLMs are widely deployed through cloud-hosted inference services, where Just-in-Time (JIT) compilation is used to reduce recurring framework and GPU-launch overhead. JIT serving introduces a host-side control plane that selects compiled artifacts and orchestrates their execution on the GPU. Meanwhile, the shared cloud setting has motivated a growing body of bit-flip attacks (BFAs) against LLM/DNN inference. Most existing BFAs ta…

Reactive Peripheral Modeling for Faithful Firmware Rehosting

arXiv:2608.29737v1 Announce Type: new Abstract: Rehosting enables tight control and introspection for firmware testing, but existing approaches largely fail to reach deeper application states and cannot drive embedded protocol stacks beyond early-stage initialization. This limitation reflects a broader weakness in current rehosting techniques: their inability to faithfully model complex peripheral semantics and dependencies. In particular, existing work typically relies on pa…

OASIS: Optimizing Attacker Sequences for Hard-Label Black-Box Text Attacks

arXiv:2608.29568v1 Announce Type: new Abstract: Different attack methods follow different search trajectories, they succeed on different subsets of samples, whereas existing hard-label black-box text attacks mainly focus on improving individual attackers or manually combining them. We present {\OURS}, a method for optimizing attacker sequences in hard-label black-box text attacks. {\OURS} first performs a one-time bi-objective attack chain search over candidate sequences to b…

Context or Digits? Balancing Memorability and Efficiency in Virtual Reality Authentication

arXiv:2608.29531v1 Announce Type: new Abstract: We present Adaptive Direction-Based Authentication (ADBA), a knowledge-based authentication method for Virtual Reality that decouples users' needs temporally by enforcing password creation based on virtual environment context while supporting both context- and digit-based entries during authentication. This design prioritizes memorability for new passwords and offers both efficient and memorable options to support users' evolvin…

SpatialTrust: A Benchmark for Environmental Risk Recognition in Secure Authentication

arXiv:2608.29489v1 Announce Type: new Abstract: Visual environmental risk recognition plays an important role in secure authentication, where a user's surroundings may reveal sensitive information or introduce potential security risks. However, existing evaluations of multimodal large language models (MLLMs) rarely examine whether models can reliably recognize, localize, and explain such risks in spatially grounded authentication scenarios. We present SpatialTrust, a question…

Benchmark Contamination: A Taxonomy Organized by Defeated Mitigation

arXiv:2608.29463v1 Announce Type: new Abstract: 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 threa…

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

arXiv:2608.29381v1 Announce Type: new Abstract: 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, a…

A Multi-Month Study of Git Commit Signing

arXiv:2608.29283v1 Announce Type: new Abstract: 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 com…

Asymmetric Phase Coding Video Watermarking

arXiv:2608.29212v1 Announce Type: new Abstract: 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;…

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

arXiv:2608.29191v1 Announce Type: new Abstract: 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 unforgeab…

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

arXiv:2608.29184v1 Announce Type: new Abstract: 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…

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

arXiv:2608.29151v1 Announce Type: new Abstract: 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 c…

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

arXiv:2608.29136v1 Announce Type: new Abstract: 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…

Mechanizing Typed Regulatory Actions for Security Tokens: Semantics, Falsification, and Bounded EVM Evidence

arXiv:2608.29134v1 Announce Type: new Abstract: Security-token standards expose privileged transfer, freezing, recovery, and compliance mechanisms, but a mechanism does not by itself identify the legal effect being executed or the evidence and reversal obligations attached to it. We formalize in Isabelle/HOL a reference execution semantics for the six regulatory-action meanings proposed in ERC-8319: FREEZE, SEIZE, CONFISCATE, LIQUIDATE, RESTRICT, and RECOVER. The model distin…

Auditing and Mitigating Privacy Leakage in Cloud-Edge Collaborative Decoding

arXiv:2608.29111v1 Announce Type: new Abstract: 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, w…

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

arXiv:2608.29084v1 Announce Type: new Abstract: 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-depend…

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

arXiv:2608.29077v1 Announce Type: new Abstract: 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, an…

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

arXiv:2608.28992v1 Announce Type: new Abstract: 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-F…

Membership is Ownership: A Robust Ownership Verification Framework for Diffusion Models

arXiv:2608.28929v1 Announce Type: new Abstract: Large-scale diffusion models have fueled numerous profitable downstream applications for AI-related businesses, including visual editing and content creation. Meanwhile, due to the huge amount of resource consumption (e.g., computation and high-quality data) during training, such diffusion models are deemed valuable intellectual property (IP) for tech companies like OpenAI and Google. Yet, the IP assets are vulnerable to various…

Identity by Design, Demographics by Accident: Demographic Leakage and Suppression in Behavioral Biometric Embeddings

arXiv:2608.28921v1 Announce Type: new Abstract: Behavioral biometric authentication (BBA) systems use deep learning models to transform biometric signals, such as eye movements, voice, keystroke/touchstroke dynamics, and gait, into identity embeddings for user authentication. While designed to encode identity, these embeddings may inadvertently reveal sensitive demographic attributes, including gender, age, and height. Consequently, an adversary with access to the authenticat…

Enhancing Web Application Firewalls with Machine Learning for SQL Injection Detection

arXiv:2608.28889v1 Announce Type: new Abstract: Detecting SQL Injection (SQLi) attacks ranks among the most critical challenges in web application security. This research conducted a systematic literature review to identify the research gaps in this domain and responsively designed and optimised a DistilBERT-Stacked Ensemble pipeline to improve detection efficiency and robustness while reducing false-positive and false-negative rates. Comprehensive pre-processing and tokenisa…

Enhancing Web Application Firewalls with BERT-GNN for SQL Injection Detection

arXiv:2608.28882v1 Announce Type: new Abstract: Detecting sophisticated SQL Injection (SQLi) attacks remains among the most critical challenges in web applications security. This research study has resulted in an optimised hybrid BERT-GNN pipeline with improved detection accuracy and robustness while reducing false-positive and false-negative rates. SQL queries are tokenised and encoded into contextual BERT embeddings, which then initialise the node features of a Graph Neural…

Breaking Darknet CAPTCHAs with general purpose LLM

arXiv:2608.28794v1 Announce Type: new Abstract: Our work evaluates the effectiveness of automated methods for solving CAPTCHA challenges commonly encountered in darknet environments. These CAPTCHAs are typically designed to operate without JavaScript, resulting in distinct characteristics compared to mainstream CAPTCHA systems. Our study considers three representative challenge types: open-circle localization, rotation-based alignment, and object-selection CAPTCHAs. The exper…

Introducing @huggingface/kernels: 200+ WebGPU Kernels for Local AI

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TAC - Rekt

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Ungentlemanly behavior: Insights into a ransomware operation

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Five plead guilty in latest federal ATM jackpotting case

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Fraudsters steal $6 million from Tectonic crypto platform after inflating token price

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North Korean Job Fraud Expands Beyond IT Into Healthcare and Sales

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How some media streaming devices open home networks to a world of harm

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OpenShot 4.0 released

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Berlin says it won’t pay ransom after hackers steal government data

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