Defense in Depth, Medieval Style

This article on the walls of Constantinople is fascinating. The system comprised four defensive lines arranged in formidable layers: The brick-lined ditch, divided by bulkheads and often flooded, 15­-20 meters wide and up to 7 meters deep. A low breastwork, about 2 meters high, enabling defenders...

GHSA-G4VJ-CJJJ-V7HG Defense in Depth update for NuGet Client

Impact This update adds validation of the package ID and version during package download, in addition to the existing package signature validation. Patches NuGet The following NuGet.exe, NuGet.CommandLine, NuGet.Packaging, and NuGet.Protocol versions have been patched: |Affected versions|Patched...

Defense in Depth update for NuGet Client

Impact This update adds validation of the package ID and version during package download, in addition to the existing package signature validation. Patches NuGet The following NuGet.exe, NuGet.CommandLine, NuGet.Packaging, and NuGet.Protocol versions have been patched: |Affected versions|Patched...

Short Message Service (SMS) Phishing Attacks and Defenses: A Systematic Review

SMS Phishing also known as 'smishing' is a growing deceptive social engineering SE attack that leverages mobile SMS to conduct cybercrimes such as stealing sensitive information or spreading malware by tricking users into interacting with attackers' messages e.g., responding to or clicking URLs...

Event-Driven Temporal Graph Networks for Asynchronous Multi-Agent Cyber Defense in NetForge_RL

The transition of Multi-Agent Reinforcement Learning MARL policies from simulated cyber wargames to operational Security Operations Centers SOCs is fundamentally bottlenecked by the Sim2Real gap. Legacy simulators abstract away network protocol physics, rely on synchronous ticks, and provide clea...

The agentic SOC—Rethinking SecOps for the next decade

Every major shift in cyberattacker behavior over the past decade has followed a meaningful shift in how defenders operate. When security operation centers SOCs deployed endpoint detection and response EDR—and later extended detection and response XDR—security teams raised the bar, pushing...

The agentic SOC—Rethinking SecOps for the next decade

Every major shift in cyberattacker behavior over the past decade has followed a meaningful shift in how defenders operate. When security operation centers SOCs deployed endpoint detection and response EDR—and later extended detection and response XDR—security teams raised the bar, pushing...

GPL Odorizers GPL750

RISK EVALUATION Successful exploitation of this vulnerability could allow a low privileged remote attacker to manipulate register values, which would result in too much or too little odorant being injected into a gas line. 2. RECOMMENDED PRACTICES CISA recommends users take defensive measures to...

Your Agent Is Mine: Measuring Malicious Intermediary Attacks on the LLM Supply Chain

Large language model LLM agents increasingly rely on third-party API routers to dispatch tool-calling requests across multiple upstream providers. These routers operate as application-layer proxies with full plaintext access to every in-flight JSON payload, yet no provider enforces cryptographic...

MCP-DPT: A Defense-Placement Taxonomy and Coverage Analysis for Model Context Protocol Security

The Model Context Protocol MCP enables large language models LLMs to dynamically discover and invoke third-party tools, significantly expanding agent capabilities while introducing a distinct security landscape. Unlike prompt-only interactions, MCP exposes pre-execution artifacts, shared context,...

SentinelSphere: Integrating AI-Powered Real-Time Threat Detection with Cybersecurity Awareness Training

The field of cybersecurity is confronted with two interrelated challenges: a worldwide deficit of qualified practitioners and ongoing human-factor weaknesses that account for the bulk of security incidents. To tackle these issues, we present SentinelSphere, a platform driven by artificial...

shadowforge

ShadowForge "Trust no one. Suspect ev...

Swiss-Bench 003: Evaluating LLM Reliability and Adversarial Security for Swiss Regulatory Contexts

The deployment of large language models LLMs in Swiss financial and regulatory contexts demands empirical evidence of both production reliability and adversarial security, dimensions not jointly operationalized in existing Swiss-focused evaluation frameworks. This paper introduces Swiss-Bench 003...

Can Drift-Adaptive Malware Detectors Be Made Robust? Attacks and Defenses under White-Box and Black-Box Threats

Concept drift and adversarial evasion are two major challenges for deploying machine learning-based malware detectors. While both have been studied separately, their combination, the adversarial robustness of drift-adaptive detectors, remains unexplored. We address this problem with AdvDA, a rece...

Your Agent, Their Asset: A Real-World Safety Analysis of OpenClaw

OpenClaw, the most widely deployed personal AI agent in early 2026, operates with full local system access and integrates with sensitive services such as Gmail, Stripe, and the filesystem. While these broad privileges enable high levels of automation and powerful personalization, they also expose...

Exploit for CVE-2025-1974

cve-2025-1974-win11-attack-defense-lab Windows 11-first educa...

SALLIE: Safeguarding against Latent Language and Image Exploits

Large Language Models LLMs and Vision-Language Models VLMs remain highly vulnerable to textual and visual jailbreaks, as well as prompt injections arXiv:2307.15043, Greshake et al., 2023, arXiv:2306.13213. Existing defenses often degrade performance through complex input transformations or treat...

Explainable Autonomous Cyber Defense Using Adversarial Multi-Agent Reinforcement Learning

Autonomous agents are increasingly deployed in both offensive and defensive cyber operations, creating high-speed, closed-loop interactions in critical infrastructure environments. Advanced Persistent Threat APT actors exploit "Living off the Land" techniques and targeted telemetry perturbations ...

CoopGuard: Stateful Cooperative Agents Safeguarding LLMs against Evolving Multi-Round Attacks

As Large Language Models LLMs are increasingly deployed in complex applications, their vulnerability to adversarial attacks raises urgent safety concerns, especially those evolving over multi-round interactions. Existing defenses are largely reactive and struggle to adapt as adversaries refine...

AttackEval: A Systematic Empirical Study of Prompt Injection Attack Effectiveness against Large Language Models

Prompt injection has emerged as a critical vulnerability in large language model LLM deployments, yet existing research is heavily weighted toward defenses. The attack side -- specifically, which injection strategies are most effective and why -- remains insufficiently studied.We address this gap...