Defining Cost Function of Steganography with Large Language Models

In this paper, we make the first attempt towards defining cost function of steganography with large language models LLMs, which is totally different from previous works that rely heavily on expert knowledge or require large-scale datasets for cost learning. To achieve this goal, a two-stage...

Chasing Shadows: Pitfalls in LLM Security Research

Large language models LLMs are increasingly prevalent in security research. Their unique characteristics, however, introduce challenges that undermine established paradigms of reproducibility, rigor, and evaluation. Prior work has identified common pitfalls in traditional machine learning researc...

Prompt injection is a problem that may never be fixed, warns NCSC

Prompt injection is shaping up to be one of the most stubborn problems in AI security, and the UK’s National Cyber Security Centre NCSC has warned that it may never be “fixed” in the way SQL injection was. Two years ago, the NCSC said prompt injection might turn out to be the “SQL injection of th...

ThinkTrap: Denial-Of-Service Attacks against Black-Box LLM Services Via Infinite Thinking

Large Language Models LLMs have become foundational components in a wide range of applications, including natural language understanding and generation, embodied intelligence, and scientific discovery. As their computational requirements continue to grow, these models are increasingly deployed as...

CISA, Australia, and Partners Author Joint Guidance on Securely Integrating Artificial Intelligence in Operational Technology

CISA and the Australian Signals Directorate’s Australian Cyber Security Centre, in collaboration with federal and international partners, have released new cybersecurity guidance: Principles for the Secure Integration of Artificial Intelligence in Operational Technology. This guidance aims to hel...

Large Language Models Cannot Reliably Detect Vulnerabilities in JavaScript: The First Systematic Benchmark and Evaluation

Researchers have proposed numerous methods to detect vulnerabilities in JavaScript, especially those assisted by Large Language Models LLMs. However, the actual capability of LLMs in JavaScript vulnerability detection remains questionable, necessitating systematic evaluation and comprehensive...

The vulnerability of the vLLM library for working with large language models involves unverified array indexing, which allows attackers to trigger service interruptions.

The vulnerability of the vLLM library for working with large language models is related to unverified array indexing. Exploiting this vulnerability could allow a malicious actor to cause service failures...

EUVD-2025-199609

NVIDIA NeMo Framework for all platforms contains a vulnerability in the NLP and LLM components, where malicious data created by an attacker could cause code injection. A successful exploit of this vulnerability may lead to code execution, escalation of privileges, information disclosure, and data...

CVE-2025-33204

NVIDIA NeMo Framework for all platforms contains a vulnerability in the NLP and LLM components, where malicious data created by an attacker could cause code injection. A successful exploit of this vulnerability may lead to code execution, escalation of privileges, information disclosure, and data...

CVE-2025-33204

NVIDIA NeMo Framework for all platforms contains a vulnerability in the NLP and LLM components, where malicious data created by an attacker could cause code injection. A successful exploit of this vulnerability may lead to code execution, escalation of privileges, information disclosure, and data...

LLM-CSEC: Empirical Evaluation of Security in C/C++ Code Generated by Large Language Models

The security of code generated by large language models LLMs is a significant concern, as studies indicate that such code often contains vulnerabilities and lacks essential defensive programming constructs. This work focuses on examining and evaluating the security of LLM-generated code,...

Defending Large Language Models against Jailbreak Exploits with Responsible AI Considerations

Large Language Models LLMs remain susceptible to jailbreak exploits that bypass safety filters and induce harmful or unethical behavior. This work presents a systematic taxonomy of existing jailbreak defenses across prompt-level, model-level, and training-time interventions, followed by three...

From Reviewers' Lens: Understanding Bug Bounty Report Invalid Reasons with LLMs

Bug bounty platforms e.g., HackerOne, BugCrowd leverage crowd-sourced vulnerability discovery to improve continuous coverage, reduce the cost of discovery, and serve as an integral complement to internal red teams. With the rise of AI-generated bug reports, little work exists to help bug hunters...

TASO: Jailbreak LLMs Via Alternative Template and Suffix Optimization

Many recent studies showed that LLMs are vulnerable to jailbreak attacks, where an attacker can perturb the input of an LLM to induce it to generate an output for a harmful question. In general, existing jailbreak techniques either optimize a semantic template intended to induce the LLM to produc...

Think Fast: Real-Time IoT Intrusion Reasoning Using IDS and LLMs at the Edge Gateway

As the number of connected IoT devices continues to grow, securing these systems against cyber threats remains a major challenge, especially in environments with limited computational and energy resources. This paper presents an edge-centric Intrusion Detection System IDS framework that integrate...

CVE-2025-62426

Summary: CVE-2025-62426 affects vLLM up to versions before 0.11.1. The /v1/chat/completions and /tokenize endpoints accept a chat_template_kwargs parameter that is used before validation, allowing an attacker to block the API server by forcing large tokenization tasks and delaying all other reque...

Steering in the Shadows: Causal Amplification for Activation Space Attacks in Large Language Models

Modern large language models LLMs are typically secured by auditing data, prompts, and refusal policies, while treating the forward pass as an implementation detail. We show that intermediate activations in decoder-only LLMs form a vulnerable attack surface for behavioral control. Building on...

PT-2025-47649

Name of the Vulnerable Software and Affected Versions vLLM versions 0.5.5 through 0.11.0 Description vLLM is an inference and serving engine for large language models LLMs. Users can cause the vLLM engine to crash when serving multimodal models by providing multimodal embedding inputs with a...

Password Strength Analysis through Social Network Data Exposure: A Combined Approach Relying on Data Reconstruction and Generative Models

Although passwords remain the primary defense against unauthorized access, users often tend to use passwords that are easy to remember. This behavior significantly increases security risks, also due to the fact that traditional password strength evaluation methods are often inadequate. In this...

GRAPHTEXTACK: A Realistic Black-Box Node Injection Attack on LLM-Enhanced GNNs

Text-attributed graphs TAGs, which combine structural and textual node information, are ubiquitous across many domains. Recent work integrates Large Language Models LLMs with Graph Neural Networks GNNs to jointly model semantics and structure, resulting in more general and expressive models that...