On Technique Identification and Threat-Actor Attribution Using LLMs and Embedding Models

Attribution of cyber-attacks remains a complex but critical challenge for cyber defenders. Currently, manual extraction of behavioral indicators from dense forensic documentation causes significant attribution delays, especially following major incidents at the international scale. This research...

Analysing Safety Risks in LLMs Fine-Tuned with Pseudo-Malicious Cyber Security Data

The integration of large language models LLMs into cyber security applications presents significant opportunities, such as enhancing threat analysis and malware detection, but can also introduce critical risks and safety concerns, including personal data leakage and automated generation of new...

Private Transformer Inference in MLaaS: a Survey

Transformer models have revolutionized AI, powering applications like content generation and sentiment analysis. However, their deployment in Machine Learning as a Service MLaaS raises significant privacy concerns, primarily due to the centralized processing of sensitive user data. Private...

SafeTrans: LLM-Assisted Transpilation from C to Rust

Rust is a strong contender for a memory-safe alternative to C as a "systems" programming language, but porting the vast amount of existing C code to Rust is a daunting task. In this paper, we evaluate the potential of large language models LLMs to automate the transpilation of C code to idiomatic...

Adversarial Attack on Large Language Models Using Exponentiated Gradient Descent

As Large Language Models LLMs are widely used, understanding them systematically is key to improving their safety and realizing their full potential. Although many models are aligned using techniques such as reinforcement learning from human feedback RLHF, they are still vulnerable to jailbreakin...

The vulnerability of the Firewall Service component in D-Link’s router microprogramming systems, DIR-618 and DIR-605L, allows a hacker to execute arbitrary code.

The vulnerability of the Firewall Service component in D-Link’s DIR-618 and DIR-605L router microprogramming systems is related to access control errors. Exploiting this vulnerability allows a remote attacker to execute arbitrary code...

The vulnerability of the traceroute utility in the microprogramming system of the RUGGEDCOM ROX routing and switching platform for models MX (MX5000, MX5000RE) and RX (RX1400, RX1501, RX1510, RX1511, RX1512, RX1524, RX1536, and RX5000) allows a hacker to execute arbitrary code.

The vulnerability of the traceroute utility in the microprogramming-based routing and switching platform RUGGEDCOM ROX for series MX MX5000, MX5000RE and RX RX1400, RX1501, RX1510, RX1511, RX1512, RX1524, RX1536, and RX5000 lies in the absence of a mechanism to verify input data on the server sid...

Palo Alto Networks PAN-OS 10.1.x < 10.1.14-h14 / 10.2.x < 10.2.11 / 11.0.x < 11.0.7 / 11.1.x < 11.1.5 Vulnerability

The version of Palo Alto Networks PAN-OS running on the remote host is 10.1.x prior to 10.1.14-h14, 10.2.x prior to 10.2.11, 11.0.x prior to 11.0.7, or 11.1.x prior to 11.1.5. It is, therefore, affected by a vulnerability. Using the AES-128-CCM algorithm for IPSec on certain Palo Alto Networks...

CVE-2025-31929

A vulnerability has been identified in IEC 1Ph 7.4kW Child socket 8EM1310-2EH04-0GA0 All versions, IEC 1Ph 7.4kW Child socket/ shutter 8EM1310-2EN04-0GA0 All versions, IEC 1Ph 7.4kW Parent cable 7m 8EM1310-2EJ04-3GA1 All versions, IEC 1Ph 7.4kW Parent cable 7m incl. SIM 8EM1310-2EJ04-3GA2 All...

Gaussian Shading++: Rethinking the Realistic Deployment Challenge of Performance-Lossless Image Watermark for Diffusion Models

Ethical concerns surrounding copyright protection and inappropriate content generation pose challenges for the practical implementation of diffusion models. One effective solution involves watermarking the generated images. Existing methods primarily focus on ensuring that watermark embedding doe...

Red Teaming the Mind of the Machine: a Systematic Evaluation of Prompt Injection and Jailbreak Vulnerabilities in LLMs

Large Language Models LLMs are increasingly integrated into consumer and enterprise applications. Despite their capabilities, they remain susceptible to adversarial attacks such as prompt injection and jailbreaks that override alignment safeguards. This paper provides a systematic investigation o...

Improved Algorithms for Differentially Private Language Model Alignment

Language model alignment is crucial for ensuring that large language models LLMs align with human preferences, yet it often involves sensitive user data, raising significant privacy concerns. While prior work has integrated differential privacy DP with alignment techniques, their performance...

Federated Large Language Models: Feasibility, Robustness, Security and Future Directions

The integration of Large Language Models LLMs and Federated Learning FL presents a promising solution for joint training on distributed data while preserving privacy and addressing data silo issues. However, this emerging field, known as Federated Large Language Models FLLM, faces significant...

LLM-Based Threat Detection and Prevention Framework for IoT Ecosystems

The increasing complexity and scale of the Internet of Things IoT have made security a critical concern. This paper presents a novel Large Language Model LLM-based framework for comprehensive threat detection and prevention in IoT environments. The system integrates lightweight LLMs fine-tuned on...

LM-Scout: Analyzing the Security of Language Model Integration in Android Apps

Developers are increasingly integrating Language Models LMs into their mobile apps to provide features such as chat-based assistants. To prevent LM misuse, they impose various restrictions, including limits on the number of queries, input length, and allowed topics. However, if the LM integration...

LiteLMGuard: Seamless and Lightweight On-Device Prompt Filtering for Safeguarding Small Language Models against Quantization-Induced Risks and Vulnerabilities

The growing adoption of Large Language Models LLMs has influenced the development of their lighter counterparts-Small Language Models SLMs-to enable on-device deployment across smartphones and edge devices. These SLMs offer enhanced privacy, reduced latency, server-free functionality, and improve...

POISONCRAFT: Practical Poisoning of Retrieval-Augmented Generation for Large Language Models

Large language models LLMs have achieved remarkable success in various domains, primarily due to their strong capabilities in reasoning and generating human-like text. Despite their impressive performance, LLMs are susceptible to hallucinations, which can lead to incorrect or misleading outputs...

On the Price of Differential Privacy for Spectral Clustering over Stochastic Block Models

We investigate privacy-preserving spectral clustering for community detection within stochastic block models SBMs. Specifically, we focus on edge differential privacy DP and propose private algorithms for community recovery. Our work explores the fundamental trade-offs between the privacy budget...

System Prompt Poisoning: Persistent Attacks on Large Language Models beyond User Injection

Large language models LLMs have gained widespread adoption across diverse applications due to their impressive generative capabilities. Their plug-and-play nature enables both developers and end users to interact with these models through simple prompts. However, as LLMs become more integrated in...

CVE-2025-30165

A flaw was found in vLLM's multi-node configuration, which is vulnerable to remote code execution due to unsafe deserialization using pickle over a ZeroMQ SUB socket. If the primary vLLM host is compromised, attackers can escalate privileges and execute arbitrary code on connected secondary hosts...