PurpCode: Reasoning for Safer Code Generation

We introduce PurpCode, the first post-training recipe for training safe code reasoning models towards generating secure code and defending against malicious cyberactivities. PurpCode trains a reasoning model in two stages: i Rule Learning, which explicitly teaches the model to reference cybersafe...

Generating Adversarial Point Clouds Using Diffusion Model

Adversarial attack methods for 3D point cloud classification reveal the vulnerabilities of point cloud recognition models. This vulnerability could lead to safety risks in critical applications that use deep learning models, such as autonomous vehicles. To uncover the deficiencies of these models...

OneShield -- the Next Generation of LLM Guardrails

The rise of Large Language Models has created a general excitement about the great potential for a myriad of applications. While LLMs offer many possibilities, questions about safety, privacy, and ethics have emerged, and all the key actors are working to address these issues with protective...

Tenda FH451 formSafeUrlFilter Function Buffer Overflow Vulnerability

The Tenda FH451 is a router from the Chinese company Tenda. The Tenda FH451 version 1.0.0.9 suffers from a buffer overflow vulnerability that originates from a failure to properly validate the length of input data for the parameter Go/page in the file /goform/SafeUrlFilter, which could be exploit...

Information Security Based on LLM Approaches: a Review

Information security is facing increasingly severe challenges, and traditional protection means are difficult to cope with complex and changing threats. In recent years, as an emerging intelligent technology, large language models LLMs have shown a broad application prospect in the field of...

Auto-SGCR: Automated Generation of Smart Grid Cyber Range Using IEC 61850 Standard Models

Digitalization of power grids have made them increasingly susceptible to cyber-attacks in the past decade. Iterative cybersecurity testing is indispensable to counter emerging attack vectors and to ensure dependability of critical infrastructure. Furthermore, these can be used to evaluate...

Regression-Aware Continual Learning for Android Malware Detection

Malware evolves rapidly, forcing machine learning ML-based detectors to adapt continuously. With antivirus vendors processing hundreds of thousands of new samples daily, datasets can grow to billions of examples, making full retraining impractical. Continual learning CL has emerged as a scalable...

LoRA-Leak: Membership Inference Attacks against LoRA Fine-Tuned Language Models

Language Models LMs typically adhere to a "pre-training and fine-tuning" paradigm, where a universal pre-trained model can be fine-tuned to cater to various specialized domains. Low-Rank Adaptation LoRA has gained the most widespread use in LM fine-tuning due to its lightweight computational cost...

Quantifying the ROI of Cyber Threat Intelligence: a Data-Driven Approach

The valuation of Cyber Threat Intelligence CTI remains a persistent challenge due to the problem of negative evidence: successful threat prevention results in non-events that generate minimal observable financial impact, making CTI expenditures difficult to justify within traditional cost-benefit...

Tab-MIA: a Benchmark Dataset for Membership Inference Attacks on Tabular Data in LLMs

Large language models LLMs are increasingly trained on tabular data, which, unlike unstructured text, often contains personally identifiable information PII in a highly structured and explicit format. As a result, privacy risks arise, since sensitive records can be inadvertently retained by the...

On One-Shot Signatures, Quantum Vs Classical Binding, and Obfuscating Permutations

One-shot signatures OSS were defined by Amos, Georgiou, Kiayias, and Zhandry STOC'20. These allow for signing exactly one message, after which the signing key self-destructs, preventing a second message from ever being signed. While such an object is impossible classically, Amos et al observe tha...

Trusted Data Fusion, Multi-Agent Autonomy, Autonomous Vehicles

Multi-agent collaboration enhances situational awareness in intelligence, surveillance, and reconnaissance ISR missions. Ad hoc networks of unmanned aerial vehicles UAVs allow for real-time data sharing, but they face security challenges due to their decentralized nature, making them vulnerable t...

CVE-2025-46686

Redis through 8.0.3 allows memory consumption via a multi-bulk command composed of many bulks, sent by an authenticated user. This occurs because the server allocates memory for the command arguments of every bulk, even when the command is skipped because of insufficient permissions. NOTE: this i...

hermes-agent

Hermes Agent ☤ The self-improving AI agent b...

CVE-2025-51471

A domain validation flaw has been discovered in Ollama. In instances where a user attempts to download a model, but where the server responds with an http 401 error code, Ollama follows the WWW-Authenticate header's realm URL without validating if it belongs to the same domain as the original...

EX-NIDS: a Framework for Explainable Network Intrusion Detection Leveraging Large Language Models

This paper introduces eX-NIDS, a framework designed to enhance interpretability in flow-based Network Intrusion Detection Systems NIDS by leveraging Large Language Models LLMs. In our proposed framework, flows labelled as malicious by NIDS are initially processed through a module called the Promp...

LLM4MEA: Data-Free Model Extraction Attacks on Sequential Recommenders Via Large Language Models

Recent studies have demonstrated the vulnerability of sequential recommender systems to Model Extraction Attacks MEAs. MEAs collect responses from recommender systems to replicate their functionality, enabling unauthorized deployments and posing critical privacy and security risks. Black-box...

CompLeak: Deep Learning Model Compression Exacerbates Privacy Leakage

Model compression is crucial for minimizing memory storage and accelerating inference in deep learning DL models, including recent foundation models like large language models LLMs. Users can access different compressed model versions according to their resources and budget. However, while existi...

AUTOPSY: a Framework for Tackling Privacy Challenges in the Automotive Industry

With the General Data Protection Regulation GDPR in place, all domains have to ensure compliance with privacy legislation. However, compliance does not necessarily result in a privacy-friendly system as for example getting users' consent to process their data does not improve the...

CVE-2025-53832

Lara Translate MCP Server is a Model Context Protocol MCP Server for Lara Translate API. Versions 0.0.11 and below contain a command injection vulnerability which exists in the @translated/lara-mcp MCP Server. The vulnerability is caused by the unsanitized use of input parameters within a call to...