ph0neutria

ph0neutria ph0neutria malware crawler v1.0.1 https://github.com/phage-nz/ph0neutria Note: This project is not actively maintained. About ph0neutria is a malware zoo builder that sources samples straight from the wild. Everything is stored in Viper for ease of access and manageability. This projec...

Enterprise Defenses Recovered at the Edge and Collapsed Inside

Enterprise defenses are tuned to catch the attacks that make noise. This year's data shows attackers winning by making none. According to Picus Labs' new Blue Report 2026, which measured more than 338 million real attack simulations across actual client production environments in the first half o...

Breaking Customized LLMs for Coding: Automated Red Teaming for Instruction Backdoor Attacks

LLM customization platforms allow users to build task-specific models for code intelligence tasks by embedding instructions into system prompts, without modifying the underlying model parameters. While these platforms lower the barrier to developing customized LLMs, they also introduce a new atta...

Malicious code in consumerweb-risk (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector aa886e4b335d3fc76fa6bc8e50c2e428ac164165830705c0ab2847857c2b3aa7 The package's preinstall lifecycle script runs automatically on npm install. It collects the installer's OS username os.userInfo.username, hostname...

MTD-Playground: An Attacker-Aware Evaluation Framework for Network Moving Target Defense

Moving Target Defense MTD has emerged as a proactive network cyber defense paradigm that increases attacker uncertainty through dynamic network reconfiguration techniques such as Software-Defined Networking SDN-enabled path randomization. However, existing evaluations remain fragmented due to...

FlipGuard: Defending Large Language Models against Quantization-Conditioned Backdoor Attacks

Model quantization is essential for the efficient deployment of Large Language Models LLMs, but introduces a critical vulnerability: Quantization-Conditioned Backdoor QCB attacks. In these attacks, malicious behaviors remain dormant in full-precision models and activate only after specific...

Your Privacy My Cloak: Backdoor Attacks on Differentially Private Federated Learning

Prior research suggests that differential privacy DP inherently enhances the robustness of federated learning FL against backdoor attacks. In this paper, we challenge this assumption. Through an empirical analysis of two baseline attack strategies, we uncover a fundamental tension in DP-FL: while...

Assessing Automated Prompt Injection Attacks in Agentic Environments

Indirect prompt injection poses a critical threat to LLM agents that interact with untrusted external data, yet automated attack methods--proven effective for jailbreaking--remain underexplored in realistic agentic settings. We present a comprehensive empirical evaluation of automated prompt...

RedEdit: Agentic Red-Teaming of Image Safety Classifiers Via MCTS-Guided Photo-Editing

Image safety classifiers serve as a critical component of contemporary content moderation systems on the internet. However, their resilience against user-style malicious image editing remains underexplored. Such behaviors are highly prevalent in daily scenarios but difficult to fully reproduce. T...

Most Remediation Programs Never Confirm the Fix Actually Worked

Security teams have never had better visibility into their environments and never been worse at confirming what they fix stays fixed. Mandiant's M-Trends 2026 report puts the mean time to exploit at an estimated negative seven days. The Verizon 2025 DBIR puts median time to remediate edge device...

ADAM: A Systematic Data Extraction Attack on Agent Memory Via Adaptive Querying

Large Language Model LLM agents have achieved rapid adoption and demonstrated remarkable capabilities across a wide range of applications. To improve reasoning and task execution, modern LLM agents would incorporate memory modules or retrieval-augmented generation RAG mechanisms, enabling them to...

Vulnerability Detection with Interprocedural Context in Multiple Languages: Assessing Effectiveness and Cost of Modern LLMs

Large Language Models LLMs have been a promising way for automated vulnerability detection. However, most prior studies have explored the use of LLMs to detect vulnerabilities only within single functions, disregarding those related to interprocedural dependencies. These studies overlook...

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...

A Defender-Attacker-Defender Model for Optimizing the Resilience of Hospital Networks to Cyberattacks

Considering the increasing frequency of cyberattacks affecting multiple hospitals simultaneously, improving resilience at a network level is essential. Various countermeasures exist to improve resilience against cyberattacks, such as deploying controls that strengthen IT infrastructures to limit...

Emoji-Based Jailbreaking of Large Language Models

Large Language Models LLMs are integral to modern AI applications, but their safety alignment mechanisms can be bypassed through adversarial prompt engineering. This study investigates emoji-based jailbreaking, where emoji sequences are embedded in textual prompts to trigger harmful and unethical...

On the Effectiveness of Instruction-Tuning Local LLMs for Identifying Software Vulnerabilities

Large Language Models LLMs show significant promise in automating software vulnerability analysis, a critical task given the impact of security failure of modern software systems. However, current approaches in using LLMs to automate vulnerability analysis mostly rely on using online API-based LL...

Evaluating LLMs for One-Shot Patching of Real and Artificial Vulnerabilities

Automated vulnerability patching is crucial for software security, and recent advancements in Large Language Models LLMs present promising capabilities for automating this task. However, existing research has primarily assessed LLMs using publicly disclosed vulnerabilities, leaving their...

How BAS Helps Threat Exposure Management: A Complete Guide

Your vulnerability scanner just produced a report with hundreds of "critical" CVEs. Now what? For most security teams, this is where the guessing game begins. You know you can't fix everything at once, so you're forced to make tough calls based on CVSS scores and gut feelings, all while hoping yo...

STAC: When Innocent Tools Form Dangerous Chains to Jailbreak LLM Agents

As LLMs advance into autonomous agents with tool-use capabilities, they introduce security challenges that extend beyond traditional content-based LLM safety concerns. This paper introduces Sequential Tool Attack Chaining STAC, a novel multi-turn attack framework that exploits agent tool use. STA...

Can Codeless Testing Tools Detect Common Security Vulnerabilities?

Learn what Codeless Testing Tools are and how effective they are in detecting common security vulnerabilities, along with understanding their strengths and limitations...