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...

LLM-Enabled Open-Source Systems in the Wild: An Empirical Study of Vulnerabilities in GitHub Security Advisories

Large language models LLMs are increasingly embedded in open-source software OSS ecosystems, creating complex interactions among natural language prompts, probabilistic model outputs, and execution-capable components. However, it remains unclear whether traditional vulnerability disclosure...

PT-2026-29877

vLLM is an inference and serving engine for large language models LLMs. From version 0.5.5 to before version 0.18.0, Librosa defaults to using numpy.mean for mono downmixing to mono, while the international standard ITU-R BS.775-4 specifies a weighted downmixing algorithm. This discrepancy result...

Combating Data Laundering in LLM Training

Data rights owners can detect unauthorized data use in large language model LLM training by querying with proprietary samples. Often, superior performance e.g., higher confidence or lower loss on a sample relative to the untrained data implies it was part of the training corpus, as LLMs tend to...

CVE-2026-27893

CVE-2026-27893 affects vLLM’s inference/serving engine. From version 0.10.1 up to (but not including) 0.18.0, two model implementation files hardcode trust_remote_code=True when loading sub-components, bypassing the user’s --trust-remote-code=False security opt-out. This enables remote code execu...

Towards Leveraging LLMs to Generate Abstract Penetration Test Cases from Software Architecture

Software architecture models capture early design decisions that strongly influence system quality attributes, including security. However, architecture-level security assessment and feedback are often absent in practice, allowing security weaknesses to propagate into later phases of the software...

CVE-2026-32114

Discourse (open‑source discussion platform) contains an Insecure Direct Object Reference (IDOR) vulnerability. Prior to versions 2026.3.0-latest.1, 2026.2.1, and 2026.1.2, any authenticated user can access metadata about AI personas, features, and LLM models by supplying their identifiers. This m...

CVE-2026-32114 Discourse's unscoped status lookups leak restricted metadata

Discourse is an open-source discussion platform. Prior to versions 2026.3.0-latest.1, 2026.2.1, and 2026.1.2, there is an Insecure Direct Object Reference IDOR vulnerability that allows any authenticated user to access metadata about AI personas, features, and LLM models by providing their...

Measuring and Exploiting Confirmation Bias in LLM-Assisted Security Code Review

Security code reviews increasingly rely on systems integrating Large Language Models LLMs, ranging from interactive assistants to autonomous agents in CI/CD pipelines. We study whether confirmation bias i.e., the tendency to favor interpretations that align with prior expectations affects LLM-bas...

Security Assessment and Mitigation Strategies for Large Language Models: A Comprehensive Defensive Framework

Large Language Models increasingly power critical infrastructure from healthcare to finance, yet their vulnerability to adversarial manipulation threatens system integrity and user safety. Despite growing deployment, no comprehensive comparative security assessment exists across major LLM...

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...

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...

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...

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...

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,...