Hybrid Privacy Policy-Code Consistency Check Using Knowledge Graphs and LLMs

The increasing concern in user privacy misuse has accelerated research into checking consistencies between smartphone apps' declared privacy policies and their actual behaviors. Recent advances in Large Language Models LLMs have introduced promising techniques for semantic comparison, but these...

CipherBank: Exploring the Boundary of LLM Reasoning Capabilities through Cryptography Challenges

Large language models LLMs have demonstrated remarkable capabilities, especially the recent advancements in reasoning, such as o1 and o3, pushing the boundaries of AI. Despite these impressive achievements in mathematics and coding, the reasoning abilities of LLMs in domains requiring cryptograph...

Graph of Attacks: Improved Black-Box and Interpretable Jailbreaks for LLMs

The challenge of ensuring Large Language Models LLMs align with societal standards is of increasing interest, as these models are still prone to adversarial jailbreaks that bypass their safety mechanisms. Identifying these vulnerabilities is crucial for enhancing the robustness of LLMs against su...

LLMpatronous: Harnessing the Power of LLMs for Vulnerability Detection

Despite the transformative impact of Artificial Intelligence AI across various sectors, cyber security continues to rely on traditional static and dynamic analysis tools, hampered by high false positive rates and superficial code comprehension. While generative AI offers promising automation...

Automatically Generating Rules of Malicious Software Packages Via Large Language Model

Today's security tools predominantly rely on predefined rules crafted by experts, making them poorly adapted to the emergence of software supply chain attacks. To tackle this limitation, we propose a novel tool, RuleLLM, which leverages large language models LLMs to automate rule generation for O...

Private Federated Learning Using Preference-Optimized Synthetic Data

In practical settings, differentially private Federated learning DP-FL is the dominant method for training models from private, on-device client data. Recent work has suggested that DP-FL may be enhanced or outperformed by methods that use DP synthetic data Wu et al., 2024; Hou et al., 2024. The...

GO-2025-3622 Mattermost doesn't restrict domains LLM can request to contact upstream in github.com/mattermost/mattermost-server

Mattermost doesn't restrict domains LLM can request to contact upstream in github.com/mattermost/mattermost-server...

DoomArena: a Framework for Testing AI Agents against Evolving Security Threats

We present DoomArena, a security evaluation framework for AI agents. DoomArena is designed on three principles: 1 It is a plug-in framework and integrates easily into realistic agentic frameworks like BrowserGym for web agents and $τ$-bench for tool calling agents; 2 It is configurable and allows...

DualBreach: Efficient Dual-Jailbreaking Via Target-Driven Initialization and Multi-Target Optimization

Recent research has focused on exploring the vulnerabilities of Large Language Models LLMs, aiming to elicit harmful and/or sensitive content from LLMs. However, due to the insufficient research on dual-jailbreaking -- attacks targeting both LLMs and Guardrails, the effectiveness of existing...

Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data

Differentially private DP machine learning often relies on the availability of public data for tasks like privacy-utility trade-off estimation, hyperparameter tuning, and pretraining. While public data assumptions may be reasonable in text and image domains, they are less likely to hold for tabul...

A Data-Centric Approach for Safe and Secure Large Language Models against Threatening and Toxic Content

Large Language Models LLM have made remarkable progress, but concerns about potential biases and harmful content persist. To address these apprehensions, we introduce a practical solution for ensuring LLM's safe and ethical use. Our novel approach focuses on a post-generation correction mechanism...

Multi-Stage Retrieval for Operational Technology Cybersecurity Compliance Using Large Language Models: a Railway Casestudy

Operational Technology Cybersecurity OTCS continues to be a dominant challenge for critical infrastructure such as railways. As these systems become increasingly vulnerable to malicious attacks due to digitalization, effective documentation and compliance processes are essential to protect these...

Everything You Wanted to Know about LLM-Based Vulnerability Detection but Were Afraid to Ask

Large Language Models are a promising tool for automated vulnerability detection, thanks to their success in code generation and repair. However, despite widespread adoption, a critical question remains: Are LLMs truly effective at detecting real-world vulnerabilities? Current evaluations, which...

Artificial Intelligence – What's all the fuss?

Talking about AI: Definitions Artificial Intelligence AI — AI refers to the simulation of human intelligence in machines, enabling them to perform tasks that typically require human intelligence, such as decision-making and problem-solving. AI is the broadest concept in this field, encompassing...

Provable Secure Steganography Based on Adaptive Dynamic Sampling

The security of private communication is increasingly at risk due to widespread surveillance. Steganography, a technique for embedding secret messages within innocuous carriers, enables covert communication over monitored channels. Provably Secure Steganography PSS is state of the art for making...

Investigating Cybersecurity Incidents Using Large Language Models in Latest-Generation Wireless Networks

The purpose of research: Detection of cybersecurity incidents and analysis of decision support and assessment of the effectiveness of measures to counter information security threats based on modern generative models. The methods of research: Emulation of signal propagation data in MIMO systems,...

Lunary 安全漏洞

lunary is lunary open source a production toolkit for LLM . An access control error vulnerability exists in lunary that stems from improper access control on the /prompts/promptid endpoint, and no detailed vulnerability details are provided at this time...

LlamaIndex 安全漏洞

LlamaIndex is a data framework for LLM applications from the LlamaIndex open source. A security vulnerability exists in LlamaIndex version v0.12.5 that stems from an unhandled thread exception and could lead to a denial of service attack...

12,000+ API Keys and Passwords Found in Public Datasets Used for LLM Training

A dataset used to train large language models LLMs has been found to contain nearly 12,000 live secrets, which allow for successful authentication. The findings once again highlight how hard-coded credentials pose a severe security risk to users and organizations alike, not to mention compounding...

Building Effective Agents with Spring AI (Part 1)

In a recent research publication: Building effective agents, Anthropic shared valuable insights about building effective Large Language Model LLM agents. What makes this research particularly interesting is its emphasis on simplicity and composability over complex frameworks. Let's explore how...