PISmith: Reinforcement Learning-Based Red Teaming for Prompt Injection Defenses

Prompt injection poses serious security risks to real-world LLM applications, particularly autonomous agents. Although many defenses have been proposed, their robustness against adaptive attacks remains insufficiently evaluated, potentially creating a false sense of security. In this work, we...

CVE-2026-27940

llama.cpp is an inference of several LLM models in C/C++. Prior to b8146, the ggufinitfromfileimpl in gguf.cpp is vulnerable to an Integer overflow, leading to an undersized heap allocation. Using the subsequent fread writes 528+ bytes of attacker-controlled data past the buffer boundary. This is...

The Attack and Defense Landscape of Agentic AI: A Comprehensive Survey

AI agents that combine large language models with non-AI system components are rapidly emerging in real-world applications, offering unprecedented automation and flexibility. However, this unprecedented flexibility introduces complex security challenges fundamentally different from those in...

VisualLeakBench: Auditing the Fragility of Large Vision-Language Models against PII Leakage and Social Engineering

As Large Vision-Language Models LVLMs are increasingly deployed in agent-integrated workflows and other deployment-relevant settings, their robustness against semantic visual attacks remains under-evaluated -- alignment is typically tested on explicit harmful content rather than privacy-critical...

FalconEYE 2.1.0

FalconEYE represents a paradigm shift in static code analysis. Instead of relying on predefined vulnerability patterns, it leverages large language models to reason about your code the same way a security expert would, understanding context, intent, and subtle security implications that tradition...

Why LLMs Fail: A Failure Analysis and Partial Success Measurement for Automated Security Patch Generation

Large Language Models LLMs show promise for Automated Program Repair APR, yet their effectiveness on security vulnerabilities remains poorly characterized. This study analyzes 319 LLM-generated security patchesacross 64 Java vulnerabilities from the Vul4J benchmark. Using tri-axis evaluation...

CVE-2026-25960

Summary of CVE-2026-25960 (vLLM) : The SSRF protection added in 0.15.1 (fix tied to CVE-2026-24779) can be bypassed in vLLM’s load_from_url_async due to inconsistent URL parsing between the validation layer (urllib3.util.parse_url) and the HTTP client (aiohttp with yarl). The vulnerability arises...

Transparent Tribe Uses AI to Mass-Produce Malware Implants in Campaign Targeting India

The Pakistan-aligned threat actor known as Transparent Tribe has become the latest hacking group to embrace artificial intelligence AI-powered coding tools to strike targets with various implants. The activity is designed to produce a "high-volume, mediocre mass of implants" that are developed...

SecureRAG-RTL: A Retrieval-Augmented, Multi-Agent, Zero-Shot LLM-Driven Framework for Hardware Vulnerability Detection

Large language models LLMs have shown remarkable capabilities in natural language processing tasks, yet their application in hardware security verification remains limited due to scarcity of publicly available hardware description language HDL datasets. This knowledge gap constrains LLM performan...

CAM-LDS: Cyber Attack Manifestations for Automatic Interpretation of System Logs and Security Alerts

Log data are essential for intrusion detection and forensic investigations. However, manual log analysis is tedious due to high data volumes, heterogeneous event formats, and unstructured messages. Even though many automated methods for log analysis exist, they usually still rely on domain-specif...

Kraken: Higher-Order EM Side-Channel Attacks on DNNs in near and Far Field

The multi-million dollar investment required for modern machine learning ML has made large ML models a prime target for theft. In response, the field of model stealing has emerged. Attacks based on physical side-channel information have shown that DNN model extraction is feasible, even on CUDA...

LLM-Assisted Deanonymization

Turns out that LLMs are good at de-anonymization: We show that LLM agents can figure out who you are from your anonymous online posts. Across Hacker News, Reddit, LinkedIn, and anonymized interview transcripts, our method identifies users with high precision ­ and scales to tens of thousands of...

Can LLMs Hack Enterprise Networks? -- Replicated Computational Results (RCR) Report

This is the Replicated Computational Results RCR Report for the paper "Can LLMs Hack Enterprise Networks?" The paper empirically investigates the efficacy and effectiveness of different LLMs for penetration-testing enterprise networks, i.e., Microsoft Active Directory Assumed-Breach Simulations...

ZeroDayBench: Evaluating LLM Agents on Unseen Zero-Day Vulnerabilities for Cyberdefense

Large language models LLMs are increasingly being deployed as software engineering agents that autonomously contribute to repositories. A major benefit these agents present is their ability to find and patch security vulnerabilities in the codebases they oversee. To estimate the capability of...

Jailbreaking Embodied LLMs Via Action-Level Manipulation

Embodied Large Language Models LLMs enable AI agents to interact with the physical world through natural language instructions and actions. However, beyond the language-level risks inherent to LLMs themselves, embodied LLMs with real-world actuation introduce a new vulnerability: instructions tha...

A Systematic Study of LLM-Based Architectures for Automated Patching

Large language models LLMs have shown promise for automated patching, but their effectiveness depends strongly on how they are integrated into patching systems. While prior work explores prompting strategies and individual agent designs, the field lacks a systematic comparison of patching...

VEcho: A Paradigm Shift from Vulnerability Verification to Proactive Discovery with Large Language Models

Static Application Security Testing SAST tools often suffer from high false positive rates, leading to alert fatigue that consumes valuable auditing resources. Recent efforts leveraging Large Language Models LLMs as filters offer limited improvements; however, these methods treat LLMs as passive,...

Vulnerability of the MarkdownRenderer.jsx component in large language models (LLMs). A new API that allows attackers to perform cross-site scripting attacks.

The vulnerability of the MarkdownRenderer.jsx component in large language models LLMs is related to the lack of measures taken to protect the structure of web pages. Exploiting this vulnerability allows a malicious actor to perform XSS attacks remotely...

LLMs Generate Predictable Passwords

LLMs are bad at generating passwords: There are strong noticeable patterns among these 50 passwords that can be seen easily: All of the passwords start with a letter, usually uppercase G, almost always followed by the digit 7. Character choices are highly uneven ­ for example, L , 9, m, 2, $ and...

Reverse CAPTCHA: Evaluating LLM Susceptibility to Invisible Unicode Instruction Injection

We introduce Reverse CAPTCHA, an evaluation framework that tests whether large language models follow invisible Unicode-encoded instructions embedded in otherwise normal-looking text. Unlike traditional CAPTCHAs that distinguish humans from machines, our benchmark exploits a capability gap: model...