SPARSEBLEED: Bypassing Attribute-Inference Defenses via Sparsity Optimizations
Deep neural networks (DNNs) are widely deployed in safety- and privacy-critical domains such as healthcare and finance,
2026-10-31 归档
今日共收录 3279 条安全情报，包含 2996 个 CVE，94 篇安全通告，以及 100 篇研究论文。
← 返回归档列表Revision-Bound Evidence Cuts: Authenticated Queries and Contextual Witness Capacity
Authentic records can support unauthorized narratives when their qualifications, query scope, or counterevidence are sil
AI-Generated Non-Consensual Intimate Imagery is Configured, Not Built: How General-Purpose AI Technology Enables Sexual Violence
AI-generated non-consensual intimate imagery (AIG-NCII) is an increasingly prevalent form of online abuse, distinguished
Set an Exploit to Catch an Exploit: Compiling Public Proofs of Concept into Threat Hunting Rules
Public exploit code for a newly disclosed vulnerability usually appears before any published rule that detects its explo
TensorBound: Characterizing Tensor Binding Failures in AI Compiler and Runtime Stacks
AI compiler and runtime stacks must preserve bindings between tensor values and their operations, storage, and execution
Heads of Security: Learned Attention Gates Improve Prompt Injection Robustness
We introduce Heads of Security (HoS), a prompt injection defense that enforces instruction-data separation via a small s
Revisiting Privacy Risks in Quantized Deep Neural Networks
Model quantization has become a fundamental technique for enabling the efficient deployment of large-scale deep neural n
When POST Becomes GET: Investigating HTTP Method Interchangeability and Its Security Implications
HTTP method semantics are a common boundary between safe retrieval and state-changing operations; however, deployed web
Penny for Your Prompts: A Measurement Study of Contextual Leakage in AI Chat Platforms
AI chatbot platforms have become one of the most popular web interfaces users interact with online, routinely receiving
SecDMP: Provably Secure Data Memory-Dependent Prefetching (Extended Version)
Data memory-dependent prefetchers (DMPs) are a microarchitectural optimization recently introduced in mainstream desktop
Poisoned Skills: Propagating Jailbreaks Through Agent Markdown Files
Modern agent frameworks allow users to provide Markdown files containing system prompts, tool descriptions, and skill de
Constraining the Commit Boundary: A DIO/ODI-Informed Authorization Guardrail for Agentic Entity Resolution
Agentic Entity Resolution (ER) systems iteratively plan actions, retrieve evidence, revise hypotheses, and propose graph
When Is a Relation Worth Trusting? Manipulation Costs, Exposure, and the Trust Trap in Multi-Relational Graph Learning
Multi-relational graph detectors learn from clean data how much to trust each relation, but an adversary pays a differen
Asynchronous Verifiable and Complete Secret Sharing: From Design Patterns to System-Level Trade-offs
Modern secret sharing protocols promise robust secret sharing in fully asynchronous Byzantine systems, but their practic
GKS Fully Homomorphic Encryption: Security Analysis
All traditional encryption schemes share a fundamental operational vulnerability: encrypted data must be decrypted befor
Janus Vehicles: Entity-Centric Threat Modelling of Protocol-Conformant Vehicles under a Malicious-Manufacturer Assumption
Automotive cybersecurity commonly assumes benign end-product manufacturers and design authorities, focusing on supply-ch
RePAIR: Inverting Backdoor Attacks in Semantic Segmentation
Despite significant advances in computer vision, semantic segmentation models remain vulnerable to backdoor attacks, pos
FVH-Bench: Evaluating Image Generator Safety Beyond Overtly Harmful Imagery
Modern image generators can produce complex visual artifacts, including diagrams, interfaces, and records that combine t
Too many clients spoil the logs: distributed parsing, aggregation techniques and metrics
Log messages are employed to identify anomalies in IoT systems and have been widely used across many applications. To en
Developing a Dynamic Multi-factor Authentication System for Securing Smart Organizations
As smart organizations increasingly rely on technologies like AI, big data, and IoT, securing these systems is crucial.