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Forensia intelligence desk · 4,099 source documents · 4,067 stories
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Are LLMs Safe Beyond Text: Do Emojis Expose Gaps in Safety Evaluation
arXiv:2608.18164v1 Announce Type: cross Abstract: Safety evaluations of large language models (LLMs) predominantly rely on text-based adversarial prompts, potentially overlooking vulnerabilities arising from alternative input representations. This work examines emoji-augmented prompts as a test case for this gap, evaluating 50 prompts across four open-source LLMs (Mistral 7B, Qwen 2 7B, Gemma 2 9B, Llama 3 8B). Results show substantial variation in robustness: Gemma 2 9B and…
Abliteration Mitigation via Refusal Aliases
arXiv:2608.18093v1 Announce Type: cross Abstract: Abliteration, the removal of refusal capabilities from large language models by projecting weight matrices orthogonal to an extracted refusal direction, has emerged as a prominent safety concern through its ability to bypass post-training alignment using only a small set of contrastive prompts. We find that existing defenses commonly overlook the cause of abliteration; that is, how easily the refusal direction can be extracted…
The Structured Totient Preimage Problem: Reconstruction, Collisions, and Cryptographic Implications
arXiv:2608.19191v1 Announce Type: new Abstract: We define and study the Structured Totient Preimage (STP) problem as a restricted reconstruction relation with a direct cryptographic motivation. Let $p_1,\ldots,p_k$ be distinct primes of the same bit length and reveal only $x=\prod_{i=1}^k(p_i-1)$. Given $(x,\lambda,k)$, STP asks for any set of $k$ distinct $\lambda$-bit primes satisfying this product. The relation is efficiently verifiable, but its reconstruction complexity i…
SiNMULI: Novel Signed Network Approach for Malicious URL Identification
arXiv:2608.19190v1 Announce Type: new Abstract: In today's era of rapid advancements in artificial intelligence, computer security and online safeguarding measures have undergone significant improvements. However, malicious websites continue to facilitate the spread of phishing schemes, fraudulent activities and unsolicited communications. Conventional methodologies in machine learning, deep learning and counterfeit website detection predominantly depend on static data analys…
FedGuard-DC: Privacy-Preserving Federated Load Forecasting and Cyber-Attack Detection for Data-Center Loads in Transmission Systems
arXiv:2608.19155v1 Announce Type: new Abstract: The rapid growth of large data-center (DC) loads is creating new challenges for power-system visibility, privacy, and cyber-physical security. System operators need accurate short-term information about these fast-varying loads, while DC operators may avoid sharing raw megawatt measurements because they can reveal sensitive workload and utilization patterns. This paper presents FedGuard-DC, a federated learning (FL) framework fo…
Autonomous Cyber Defense in Connected Vehicles: A Multi-Agent Approach to V2X Security
arXiv:2608.19135v1 Announce Type: new Abstract: A connected vehicle has roughly 100 milliseconds to decide whether an incoming Basic Safety Message is real or fabricated. If a false emergency braking alert reaches the planning pipeline in time, the car brakes - a safety failure triggered by a security failure. Existing intrusion detection systems are not designed to handle that coupling. They operate per vehicle, per message, with static rules - blind to attack patterns that…
Toward Quantum Advantage in Learning Parities with Structured Noise via Lower Bound Optimization of the Condition Number
arXiv:2608.19122v1 Announce Type: new Abstract: Learning Parities with Structured Noise (LPSN) can be reduced to solving nonlinear Boolean systems. In quantum computing, such systems are typically transformed into Macaulay linear systems and solved via quantum linear system algorithms, a process severely limited by the condition number. To address this, we propose a novel reduction method for Macaulay linear systems. Under the assumptions of Ding et al., we derive a condition…
Malformer: A Multi-Modal Malware Detector Using Transformers
arXiv:2608.19052v1 Announce Type: new Abstract: Traditional malware detection systems that rely on a single representation of malware often fail to identify novel threats. These representations of malware binaries, also known as modalities, do not provide the models with sufficient information to discriminate among all samples. Additionally, individual representations introduce new failure modes, with some modality extraction being dependent upon the success of disassembling.…
From Threat Intelligence to Detection: Knowledge-driven Enrichment and Template-based Rule Grounding for Automated Sigma Rule Generation
arXiv:2608.19011v1 Announce Type: new Abstract: Mechanisms for dynamically converting cyber threat intelligence (CTI) into actionable detection capabilities are necessary due to the rapid evolution of Advanced Persistent Threats (APTs). Sigma rules are an essential part of contemporary threat detection workflows because they offer a platform-independent framework for expressing detection logic that can be converted into particular queries across SIEM systems. Conventional tec…
A 12-Step Process for Industrial Internet of Things (IIoT) Forensics
arXiv:2608.18991v1 Announce Type: new Abstract: The increasing deployment of the Industrial Internet of Things (IIoT) in critical infrastructure sectors like manufacturing, healthcare, and transportation has shown new challenges for Digital Forensics (DF). Traditional DF methodologies are not well equipped to handle the complexity, scale, and heterogeneity of IIoT environments. This paper introduces a comprehensive Twelve-Step Process (TSP) tailored specifically for IIoT inci…
Catastrophic Learning: A New Attack Vector on Continual Learning Networks
arXiv:2608.18976v1 Announce Type: new Abstract: Continual Learning (CL) enables deep learning models to iteratively learn from a stream of data without forgetting prior knowledge. Existing adversarial research on CL primarily aims to re-enable catastrophic forgetting, attacking stability and reducing availability. We identify a novel security flaw: data manipulated by an attacker can reduce the learnability of current or upcoming iterations. We term such manipulations learnin…
Who Can Make the Action Happen? An Authority-Decomposition Framework for High-Risk Automated Systems
arXiv:2608.18965v1 Announce Type: new Abstract: High-risk automated systems distribute control across services, credentials, protected components, and lifecycle mechanisms. Labels such as authorized, approved, privileged, or protected therefore do not answer a basic causal question: which actors can actually make a consequential action occur? This paper provides an action-relative method for deriving which trust-domain coalitions are sufficient to cause protected execution, d…
CauSec: Unboxing the Causal Drivers of Static Vulnerability Analysis Performance
arXiv:2608.18876v1 Announce Type: new Abstract: Static Application Security Testing (SAST) tools are widely used in both industry and academia. Such tools often make design choices that sacrifice detection to achieve higher performance, i.e., increased precision, decreased runtime, or increased scalability. These design choices rely on certain assumptions regarding the target code or the analysis technique itself. Hence, the assumptions directly impact the detection outcome t…
Improving LLM-Based SSH Honeypots Through Prompting and Fine-Tuning
arXiv:2608.18686v1 Announce Type: new Abstract: LLM-based SSH honeypots often use closed cloud LLMs because they give strong shell realism, but cloud models create deployment problems. These include no stable versioning, provider-side changes, attacker-driven cost, and model decommissioning. Local open-weight models avoid these problems, but they usually perform worse and make mistakes that reveal the honeypot. These mistakes include malformed outputs, command echoing, incons…
IriSig-Spoof: A Real-World Benchmark for Time-Robust Satellite RF Fingerprinting and Spoofing Detection
arXiv:2608.18642v1 Announce Type: new Abstract: Low Earth orbit (LEO) satellite Internet is becoming critical communications infrastructure, yet its open wireless links remain vulnerable to satellite impersonation and signal spoofing. Radio frequency fingerprinting (RFF) offers a potential defense by exploiting transmitter-specific hardware imperfections manifested in received signals. However, the reliability of existing satellite RFF methods remains difficult to assess beca…
Finality Before Disclosure for Ledger Authenticators in the Quantum Random Oracle Model
arXiv:2608.18605v1 Announce Type: new Abstract: Public ledgers increasingly authorize state transitions using prior transactions, finalized state, timing, and ordering rather than only a public key, message, and portable signature. We introduce ledger authenticators and $\LAEUF$, an unforgeability experiment for reactive authorization protocols whose public judgment algorithm reads a finalized transcript. The model separates authentication safety from ledger liveness and capt…
VQC-ZTI: Variational Quantum Control for Zero Trust Protection of the Tactile Internet
arXiv:2608.18572v1 Announce Type: new Abstract: Tactile Internet services couple cyber events directly to physical actuation, so security decisions must improve risk discrimination without perturbing the control path. This paper presents VQC-ZTI, a split-plane Variational Quantum Classifier framework for zero-trust protection of Tactile Internet services, in which an off-path VQC analyzes encrypted-flow telemetry while an on-path policy engine applies cached deterministic gra…
Beyond Distortion Robustness: Rethinking Severe Cropping as Erasure-Resilient Message Embedding
arXiv:2608.18567v1 Announce Type: new Abstract: Robust message embedding in images is important for multimedia security applications such as copyright protection and content tracing. Existing methods are largely developed under a distortion robustness paradigm, where the embedded signal remains spatially present but is degraded by noise, blur, or compression. Severe cropping poses a fundamentally different challenge because it removes part of the carrier itself, causing parti…
AoNT Trap: Borromean-Entangled Mutable Chameleon Trapdoor Hash All-or-Nothing Stream Cipher
arXiv:2608.18403v1 Announce Type: new Abstract: This work introduces the Borromean-Entangled Chameleon Trapdoor Hash All-or-Nothing (AoNT) Stream Cipher (BEC-Trap), a novel construction that merges Borromean interdependence, trapdoor-enabled mutability, and streaming encryption into a unified framework. The (BEC-Trap) cipher links key (K), initialization vector (V ), and internal state (St) in a Borromean structure, ensuring that breaking, guessing, or removing any one compon…
0xPass: A Secure Protocol for Universal Cross-Chain Accounts
arXiv:2608.18359v1 Announce Type: new Abstract: Universal accounts allow users to manage assets and execute operations across heterogeneous blockchain ecosystems through a single interface, but they introduce security and trust challenges involving authentication, authorization, transaction signing, key custody, recovery, and decentralization. This paper presents 0xPass, a modular protocol architecture for universal cross-chain accounts. 0xPass separates request orchestration…
Task-Conditioned Least-Privilege Learning for Executable Terminal and MCP Agents
arXiv:2608.18351v1 Announce Type: new Abstract: Tool-using large language-model agents can complete a task while exercising authority that the user did not grant or the task does not need, causing excess-authority errors. Traditional permission gating systems alone for validating agent environments are insufficient. We study whether post-training can teach a 4B-parameter model to choose task-conditioned authority in executable terminal and Model Context Protocol (MCP) environ…
XNET: Intelligent Dynamic Sampling for High-Speed Network Security Monitoring
arXiv:2608.18349v1 Announce Type: new Abstract: Growing network speeds, with 100GbE line rates becoming common in modern enterprise networks, pose challenges to operators and security applications, as they struggle to scale their operational efficiency accordingly, without relying on costly hardware, excessive sampling, or complex distributed deployments. Unintentional loss due to stochastic packet sampling often produces low-quality traffic, further risking missed detection…
Model Card for OpenAI Privacy Filter
arXiv:2608.18274v1 Announce Type: new Abstract: OpenAI Privacy Filter is a compact, bidirectional token-classification model for detecting and redacting personally identifiable information (PII) and secrets in unstructured text. The model is derived from an autoregressively pretrained checkpoint and converted into a bidirectional, banded-attention classifier that labels an input sequence in a single forward pass. A constrained Viterbi decoder produces coherent spans across ei…
AdaRare: Telemetry-Guided Joint Profile Control for Greybox Fuzzing
arXiv:2608.18187v1 Announce Type: new Abstract: Greybox fuzzers combine interacting queue, mutation, dictionary, energy, and comparison-solving control surfaces, while prior adaptive systems typically optimize other decision objects or control layers. We present AdaRare, an AFL++ extension that coordinates five internal actuation mechanisms as one bounded in-process profile updated every 5,000 ms. Completed-window, action-induced telemetry feeds an arm-local recency-weighted…
[$] LWN.net Weekly Edition for August 20, 2026
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Blockchain Intelligence Hunger Games
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Cloudflare Workers Spectre Attack Leaks JWT From Co-Located Worker at 12 Bits/Second
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Go 1.27 released
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Electronic health record company CareCloud says 3.7 million people affected by breach
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OpenAI Pauses Frontier RL Training as It Tightens Defenses Against Unsafe AI Behavior
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Seven stable kernels for Wednesday
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NSA, FBI warns of hackers using AI-generated tools in attacks on critical infrastructure technology
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[$] Debian weighs eight options in vote on LLM usage
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Microsoft named a Leader in the Frost Radar™: Cloud Workload Protection Platforms, 2026
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Tuba 0.11 released
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[$] Representing Python paths using pathlib
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LFM2.5 Q4\_0 Checkpoints from Quantization-Aware Distillation
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Latvian officials resign after cyberattack exposes data on 1.2 million people
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Virtual Event Today: CodeSecCon – Secure Your Code and Applications
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SilkParasite Espionage Campaign Targets Central Asian Governments with Five New RATs
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clustered evidenceWatchGuard Patches Critical Vulnerabilities
developing · new
Microsoft Rolls Out 22 Fresh Security Patches
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Google Patches 6th Chrome Zero-Day of 2026
developing · new
Cisco Warns of Unpatched Secure Email Flaws, Patches Critical Switch Vulnerabilities
developing · new
Cisco Patches Critical Crosswork, Secure Workload Vulnerabilities
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