Dual Explanations Via Subgraph Matching for Malware Detection

Interpretable malware detection is crucial for understanding harmful behaviors and building trust in automated security systems. Traditional explainable methods for Graph Neural Networks GNNs often highlight important regions within a graph but fail to associate them with known benign or maliciou...

New Capacity Bounds for PIR on Graph and Multigraph-Based Replicated Storage

In this paper, we study the problem of private information retrieval PIR in both graph-based and multigraph-based replication systems, where each file is stored on exactly two servers, and any pair of servers shares at most $r$ files. We derive upper bounds on the PIR capacity for such systems an...

FCGHunter: Towards Evaluating Robustness of Graph-Based Android Malware Detection

Graph-based detection methods leveraging Function Call Graphs FCGs have shown promise for Android malware detection AMD due to their semantic insights. However, the deployment of malware detectors in dynamic and hostile environments raises significant concerns about their robustness. While recent...

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...

PICO: Secure Transformers Via Robust Prompt Isolation and Cybersecurity Oversight

We propose a robust transformer architecture designed to prevent prompt injection attacks and ensure secure, reliable response generation. Our PICO Prompt Isolation and Cybersecurity Oversight framework structurally separates trusted system instructions from untrusted user inputs through dual...

PT-2025-17701 · Mattermost · Mattermost

Name of the Vulnerable Software and Affected Versions: Mattermost versions 9.11.x through 9.11.10 Mattermost versions 10.4.x through 10.4.2 Mattermost versions 10.5.x through 10.5.0 Description: The issue arises from the failure to validate the uniqueness and quantity of task actions within the...

Fishing for Phishers: Learning-Based Phishing Detection in Ethereum Transactions

Phishing detection on Ethereum has increasingly leveraged advanced machine learning techniques to identify fraudulent transactions. However, limited attention has been given to understanding the effectiveness of feature selection strategies and the role of graph-based models in enhancing detectio...

Cluster-Aware Attacks on Graph Watermarks

Data from domains such as social networks, healthcare, finance, and cybersecurity can be represented as graph-structured information. Given the sensitive nature of this data and their frequent distribution among collaborators, ensuring secure and attributable sharing is essential. Graph...

Integrating Graph Theoretical Approaches in Cybersecurity Education CSCI-RTED

As cybersecurity threats continue to evolve, the need for advanced tools to analyze and understand complex cyber environments has become increasingly critical. Graph theory offers a powerful framework for modeling relationships within cyber ecosystems, making it highly applicable to cybersecurity...

On the Consistency of GNN Explanations for Malware Detection

Control Flow Graphs CFGs are critical for analyzing program execution and characterizing malware behavior. With the growing adoption of Graph Neural Networks GNNs, CFG-based representations have proven highly effective for malware detection. This study proposes a novel framework that dynamically...

A Review on Privacy in DAG-Based DLTs

Directed Acyclic Graph DAG-based Distributed Ledger Technologies DLTs have emerged as a promising solution to the scalability issues inherent in traditional blockchains. However, amidst the focus on scalability, the crucial aspect of privacy within DAG-based DLTs has been largely overlooked. This...

ScaloWork: Useful Proof-Of-Work with Distributed Pool Mining

Bitcoin blockchain uses hash-based Proof-of-Work PoW that prevents unwanted participants from hogging the network resources. Anyone entering the mining game has to prove that they have expended a specific amount of computational power. However, the most popular Bitcoin blockchain consumes 175.87...

SUSE CVE-2025-39930

In the Linux kernel, the following vulnerability has been resolved: ASoC: simple-card-utils: Don't use freedevicenode at graphutilparsedai commit 419d1918105e "ASoC: simple-card-utils: use freedevicenode for device node" uses freedevicenode for dlc-ofnode, but we need to keep it while driver is i...

CVE-2025-39930

In the Linux kernel, the following vulnerability has been resolved: ASoC: simple-card-utils: Don't use freedevicenode at graphutilparsedai commit 419d1918105e "ASoC: simple-card-utils: use freedevicenode for device node" uses freedevicenode for dlc-ofnode, but we need to keep it while driver is i...

UBUNTU-CVE-2025-39930

In the Linux kernel, the following vulnerability has been resolved: ASoC: simple-card-utils: Don't use freedevicenode at graphutilparsedai commit 419d1918105e "ASoC: simple-card-utils: use freedevicenode for device node" uses freedevicenode for dlc-ofnode, but we need to keep it while driver is i...

CVE-2025-39930 ASoC: simple-card-utils: Don't use __free(device_node) at graph_util_parse_dai()

In the Linux kernel, the following vulnerability has been resolved: ASoC: simple-card-utils: Don't use freedevicenode at graphutilparsedai commit 419d1918105e "ASoC: simple-card-utils: use freedevicenode for device node" uses freedevicenode for dlc-ofnode, but we need to keep it while driver is i...

Designing a Reliable Lateral Movement Detector Using a Graph Foundation Model

Foundation models have recently emerged as a new paradigm in machine learning ML. These models are pre-trained on large and diverse datasets and can subsequently be applied to various downstream tasks with little or no retraining. This allows people without advanced ML expertise to build ML...

PANO - Advanced OSINT Investigation Platform Combining Graph Visualization, Timeline Analysis, And AI Assistance To Uncover Hidden Connections In Data

PANO is a powerful OSINT investigation platform that combines graph visualization, timeline analysis, and AI-powered tools to help you uncover hidden connections and patterns in your data. Getting Started 1. Clone the repository: bash git clone https://github.com/ALW1EZ/PANO.git cd PANO 2. Run th...

GraphQLer: Enhancing GraphQL Security with Context-Aware API Testing

GraphQL is an open-source data query and manipulation language for web applications, offering a flexible alternative to RESTful APIs. However, its dynamic execution model and lack of built-in security mechanisms expose it to vulnerabilities such as unauthorized data access, denial-of-service DoS...

GraphAttack: Exploiting Representational Blindspots in LLM Safety Mechanisms

Large Language Models LLMs have been equipped with safety mechanisms to prevent harmful outputs, but these guardrails can often be bypassed through "jailbreak" prompts. This paper introduces a novel graph-based approach to systematically generate jailbreak prompts through semantic transformations...