AI Researchers Must Help Lead Arms Control to Mitigate Military AI Risks

The advancement of AI capabilities compels researchers and the public to be more aware of its potential worldwide impact. A pressing near-term concern is the regulation of military AI applications. Armament manufacturers and defense contractors are increasingly investing in AI capabilities and...

xss-defense-system

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Model Poisoning against Federated Model Adaptation with Chain of Bit-Flips

Federated Learning FL allows a set of clients to collectively train a global model without sharing local training data. Giving the responsibility of the training to decentralized actors may lead to poisoning attacks: clients controlled by malicious third party potentially poison the training...

Closing the Sim-To-Real Gap: An Evaluation Framework for Autonomous Cyber Defense Configuration of Commercial EDR

Leading commercial endpoint detection and response EDR products have shifted from operator-configured rule sets to multi-component systems where autonomous AI components operate alongside, and increasingly in place of, operator-deployed policies. Autonomous defense agents using commercial EDR as...

Hiding in Plain Floats: Steganographic Carriers for Indirect Prompt and Content Injection

Text-centered prompt-injection defenses assume that the malicious signal is visible in one of the inspected text views. We study a reproducible LLM01-style indirect prompt/content-injection failure mode where that assumption breaks: a payload caught in plain English slips past the same detector...

Beyond Pass/Fail: Using Process Mining to Understand How LLMs Resist (And Fail) Red Team Attacks

Standard AI red teaming evaluations reduce adversarial campaigns to a single binary outcome, attack success rate ASR, not taking into account the sequential structure of how models resist or yield to attacks. We propose applying process mining, a discipline for discovering and analyzing process...

Quarterly WordPress Threat Intelligence Report – Q1 2026

As the leader in WordPress security, Wordfence provides unparalleled security coverage that fully encompasses protection, active monitoring, detection, and response all built around our threat intelligence, demonstrating a strong commitment to security. Our mission is to ensure comprehensive...

Agentic AI Is Transforming Defense, But Only Secure IT Infrastructure Will Maximize It

Over the past several weeks, the cybersecurity community has been reminded how quickly frontier and agentic AI in defense networks can challenge our assumptions. When Anthropic's Claude Mythos model was made available to a limited set of organizations as a technical preview, it was reported that ...

SmartMES-Range

SmartMES Attack-Defense Drill Site The Smart Manufacturing En...

Membrane: A Self-Evolving Contrastive Safety Memory for LLM Agent Defense

Despite advances in safety alignment, large language models remain vulnerable to continuously evolving jailbreaks. Existing fine-tuned safety classifiers cannot adapt to these evolving attacks, while adaptive memory-based guardrails tend to over-refuse benign queries that resemble stored attacks...

Steering LLM Viewpoints through Fabricated Evidence Injection

As chatbots increasingly influence daily decision-making, their potential to produce misleading responses poses substantial risks to users. This paper investigates a critical cognitive vulnerability in LLMs: their tendency to uncritically trust external context when presented with fabricated...

Exploit for Stack-based Buffer Overflow in Microsoft

CVE-2026-41089 — SentinelCore Defensive Toolkit !Statushtt...

The Intersection of Encryption and AI

As part of their 20th Anniversary celebration, Dark Reading asked five cybersecurity industry leaders who wrote blogs or columns for them over the years to select their favorite piece and share their reflections on the topic today. This is my section. Renowned technologist and author Bruce Schnei...

AI Model Extraction Attacks: Bypassing Single-Client Assumptions in Defenses

Ensuring the protection of Artificial Intelligence AI models deployed in military Command and Control C2 systems and critical infrastructure is essential for maintaining information superiority. Model Extraction Attacks MEAs pose a significant threat, as they enable adversaries to replicate...

Backdoor Unlearning Generalization: A Path toward the Removal of Unknown Triggers in LLMs

Backdoor attacks in Large Language Models LLMs are a growing security concern, where models can generate adversary-chosen content. Existing defenses target backdoors one at a time and typically require knowledge of the trigger, leaving the defender at a structural disadvantage when unknown...

Operationalizing Cyber Attack Prediction: A Gap-Prioritized Framework with Dataset and Model Selection Guidelines

While AI and machine learning for cyber attack prediction have advanced, a critical gap persists between theoretical research and practical operational deployment. Building on Ankalaki et al. 2025, this paper provides a comprehensive analysis of 150+ benchmark datasets and 200+ studies to identif...

snyk-agentic-appsec-poc

Snyk Agentic AppSec POC Proof of concept demonstrating autono...

EUVD-2026-33597

A bug in the login redirect route in Apache Airflow allowed authenticated users to craft URLs that bypassed the issafeurl check, enabling redirection from a trusted Airflow domain to an attacker-controlled origin. Users are advised to upgrade to apache-airflow 3.2.2 or later. As a defense-in-dept...

Patcher: Post-Hoc Patching of Backdoored Large Language Models

Large language models remain vulnerable to jailbreak backdoor attacks, where adversaries poison safety alignment data to embed hidden triggers that bypass safety mechanisms. Existing defenses often require comprehensive attack information or multiple triggered examples, making them impractical wh...

Defenses and Enablers for Skill Injection Attacks on Terminal Based Agents

Large language model LLM agents increasingly rely on reusable skills i.e. documents describing task-specific procedures. However, this introduces a new attack surface for agents to manage. We study two complementary directions for this threat. First, we evaluate guardian-based defenses: an...