694 matches found
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...
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,...
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...
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...
APFuzz: Towards Automatic Greybox Protocol Fuzzing
Greybox protocol fuzzing is a random testing approach for stateful protocol implementations, where the input is protocol messages generated from mutations of seeds, and the search in the input space is driven by the feedback on coverage of both code and state. State model and message model are th...
Analysis of LLMs against Prompt Injection and Jailbreak Attacks
Large Language Models LLMs are widely deployed in real-world systems. Given their broader applicability, prompt engineering has become an efficient tool for resource-scarce organizations to adopt LLMs for their own purposes. At the same time, LLMs are vulnerable to prompt-based attacks. Thus,...
AdapTools: Adaptive Tool-Based Indirect Prompt Injection Attacks on Agentic LLMs
The integration of external data services e.g., Model Context Protocol, MCP has made large language model-based agents increasingly powerful for complex task execution. However, this advancement introduces critical security vulnerabilities, particularly indirect prompt injection IPI attacks...
An Explainable Memory Forensics Approach for Malware Analysis
Memory forensics is an effective methodology for analyzing living-off-the-land malware, including threats that employ evasion, obfuscation, anti-analysis, and steganographic techniques. By capturing volatile system state, memory analysis enables the recovery of transient artifacts such as decrypt...
LLM-Enabled Applications Require System-Level Threat Monitoring
LLM-enabled applications are rapidly reshaping the software ecosystem by using large language models as core reasoning components for complex task execution. This paradigm shift, however, introduces fundamentally new reliability challenges and significantly expands the security attack surface, du...
TFL: Targeted Bit-Flip Attack on Large Language Model
Large language models LLMs are increasingly deployed in safety and security critical applications, raising concerns about their robustness to model parameter fault injection attacks. Recent studies have shown that bit-flip attacks BFAs, which exploit computer main memory i.e., DRAM vulnerabilitie...
