Astra Linux - уязвимость в yajl

yajl-ruby is a C binding to the YAJL JSON parsing and generation library. The 1.x branch and the 2.x branch of yajl contain an integer overflow which leads to subsequent heap memory corruption when dealing with large 2GB inputs. The reallocation logic at yajlbuf.cL64 may result in the need 32bit...

Security update for openssh

This update for openssh fixes the following issues: Security issues fixed: CVE-2025-32728: Fixed a logic error in DisableForwarding option bsc1241012 Other bugs fixed: Allow KEX hashes greater than 256 bits bsc1241045 Fixed hostname being left out of the audit output bsc1228634 Fixed failures wit...

SUSE-SU-2025:1576-1 Security update for openssh

This update for openssh fixes the following issues: - Security issues fixed: CVE-2025-32728: Fixed a logic error in DisableForwarding option bsc1241012 - Other bugs fixed: Allow KEX hashes greater than 256 bits bsc1241045 Fixed hostname being left out of the audit output bsc1228634 Fixed failures...

One Shot Dominance: Knowledge Poisoning Attack on Retrieval-Augmented Generation Systems

Large Language Models LLMs enhanced with Retrieval-Augmented Generation RAG have shown improved performance in generating accurate responses. However, the dependence on external knowledge bases introduces potential security vulnerabilities, particularly when these knowledge bases are publicly...

Fragments to Facts: Partial-Information Fragment Inference from LLMs

Large language models LLMs can leak sensitive training data through memorization and membership inference attacks. Prior work has primarily focused on strong adversarial assumptions, including attacker access to entire samples or long, ordered prefixes, leaving open the question of how vulnerable...

MorphMark: Flexible Adaptive Watermarking for Large Language Models

Watermarking by altering token sampling probabilities based on red-green list is a promising method for tracing the origin of text generated by large language models LLMs. However, existing watermark methods often struggle with a fundamental dilemma: improving watermark effectiveness the...

Benchmarking LLMs in an Embodied Environment for Blue Team Threat Hunting

As cyber threats continue to grow in scale and sophistication, blue team defenders increasingly require advanced tools to proactively detect and mitigate risks. Large Language Models LLMs offer promising capabilities for enhancing threat analysis. However, their effectiveness in real-world blue...

The Impact of Emerging Phishing Threats: Assessing Quishing and LLM-Generated Phishing Emails against Organizations

Modern organizations are persistently targeted by phishing emails. Despite advances in detection systems and widespread employee training, attackers continue to innovate, posing ongoing threats. Two emerging vectors stand out in the current landscape: QR-code baits and LLM-enabled pretexting. Yet...

MalVis: a Large-Scale Image-Based Framework and Dataset for Advancing Android Malware Classification

As technology advances, Android malware continues to pose significant threats to devices and sensitive data. The open-source nature of the Android OS and the availability of its SDK contribute to this rapid growth. Traditional malware detection techniques, such as signature-based, static, and...

On Membership Inference Attacks in Knowledge Distillation

Nowadays, Large Language Models LLMs are trained on huge datasets, some including sensitive information. This poses a serious privacy concern because privacy attacks such as Membership Inference Attacks MIAs may detect this sensitive information. While knowledge distillation compresses LLMs into...

ProxyPrompt: Securing System Prompts against Prompt Extraction Attacks

The integration of large language models LLMs into a wide range of applications has highlighted the critical role of well-crafted system prompts, which require extensive testing and domain expertise. These prompts enhance task performance but may also encode sensitive information and filtering...

AutoRAN: Weak-To-Strong Jailbreaking of Large Reasoning Models

This paper presents AutoRAN, the first automated, weak-to-strong jailbreak attack framework targeting large reasoning models LRMs. At its core, AutoRAN leverages a weak, less-aligned reasoning model to simulate the target model's high-level reasoning structures, generates narrative prompts, and...

S3C2 Summit 2024-09: Industry Secure Software Supply Chain Summit

While providing economic and software development value, software supply chains are only as strong as their weakest link. Over the past several years, there has been an exponential increase in cyberattacks, specifically targeting vulnerable links in critical software supply chains. These attacks...

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...

Cape: Context-Aware Prompt Perturbation Mechanism with Differential Privacy

Large Language Models LLMs have gained significant popularity due to their remarkable capabilities in text understanding and generation. However, despite their widespread deployment in inference services such as ChatGPT, concerns about the potential leakage of sensitive user data have arisen...

Cutting through Privacy: a Hyperplane-Based Data Reconstruction Attack in Federated Learning

Federated Learning FL enables collaborative training of machine learning models across distributed clients without sharing raw data, ostensibly preserving data privacy. Nevertheless, recent studies have revealed critical vulnerabilities in FL, showing that a malicious central server can manipulat...

Managerial Insights on Investment Strategy in Cybersecurity: Findings from Multi-Country Research

This study examines the strategic role of cybersecurity based on survey data from 1,083 managers across Europe, the UK, and the United States. The findings indicate growing recognition of cybersecurity as a source of competitive advantage, although firms continue to face barriers such as limited...

Automating Security Audit Using Large Language Model Based Agent: an Exploration Experiment

In the current rapidly changing digital environment, businesses are under constant stress to ensure that their systems are secured. Security audits help to maintain a strong security posture by ensuring that policies are in place, controls are implemented, gaps are identified for cybersecurity...

rubygem-rack: Unbounded-Parameter DoS in Rack::QueryParser

A flaw was found in Rack::QueryParser. This vulnerability allows denial of service via oversized HTTP requests containing many parameters, resulting in memory exhaustion that consumes all available memory or CPU resource pinning, which keeps the CPU constantly busy...

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...