Predicting Known Vulnerabilities from Attack Descriptions Using Sentence Transformers

Modern infrastructures rely on software systems that remain vulnerable to cyberattacks. These attacks frequently exploit vulnerabilities documented in repositories such as MITRE's Common Vulnerabilities and Exposures CVE. However, Cyber Threat Intelligence resources, including MITRE ATT&CK and CV...

SeBERTis: A Framework for Producing Classifiers of Security-Related Issue Reports

Monitoring issue tracker submissions is a crucial software maintenance activity. A key goal is the prioritization of high risk, security-related bugs. If such bugs can be recognized early, the risk of propagation to dependent products and endangerment of stakeholder benefits can be mitigated. To...

Evaluating Large Language Models in Detecting Secrets in Android Apps

Mobile apps often embed authentication secrets, such as API keys, tokens, and client IDs, to integrate with cloud services. However, developers often hardcode these credentials into Android apps, exposing them to extraction through reverse engineering. Once compromised, adversaries can exploit...

Automated Cyber Defense with Generalizable Graph-Based Reinforcement Learning Agents

Deep reinforcement learning RL is emerging as a viable strategy for automated cyber defense ACD. The traditional RL approach represents networks as a list of computers in various states of safety or threat. Unfortunately, these models are forced to overfit to specific network topologies, renderin...

A Non-Monotonic Relationship: an Empirical Analysis of Hybrid Quantum Classifiers for Unseen Ransomware Detection

Detecting unseen ransomware is a critical cybersecurity challenge where classical machine learning often fails. While Quantum Machine Learning QML presents a potential alternative, its application is hindered by the dimensionality gap between classical data and quantum hardware. This paper...

Multilingual Source Tracing of Speech Deepfakes: a First Benchmark

Recent progress in generative AI has made it increasingly easy to create natural-sounding deepfake speech from just a few seconds of audio. While these tools support helpful applications, they also raise serious concerns by making it possible to generate convincing fake speech in many languages...

Proactive Disentangled Modeling of Trigger-Object Pairings for Backdoor Defense

Deep neural networks DNNs and generative AI GenAI are increasingly vulnerable to backdoor attacks, where adversaries embed triggers into inputs to cause models to misclassify or misinterpret target labels. Beyond traditional single-trigger scenarios, attackers may inject multiple triggers across...

Few-Shot Learning-Based Cyber Incident Detection with Augmented Context Intelligence

In recent years, the adoption of cloud services has been expanding at an unprecedented rate. As more and more organizations migrate or deploy their businesses to the cloud, a multitude of related cybersecurity incidents such as data breaches are on the rise. Many inherent attributes of cloud...

Efficient Malware Detection with Optimized Learning on High-Dimensional Features

Malware detection using machine learning requires feature extraction from binary files, as models cannot process raw binaries directly. A common approach involves using LIEF for raw feature extraction and the EMBER vectorizer to generate 2381-dimensional feature vectors. However, the high...

Membership Inference Attacks for Unseen Classes

Shadow model attacks are the state-of-the-art approach for membership inference attacks on machine learning models. However, these attacks typically assume an adversary has access to a background nonmember data distribution that matches the distribution the target model was trained on. We initiat...

Sentinel: SOTA Model to Protect against Prompt Injections

Large Language Models LLMs are increasingly powerful but remain vulnerable to prompt injection attacks, where malicious inputs cause the model to deviate from its intended instructions. This paper introduces Sentinel, a novel detection model, qualifire/prompt-injection-sentinel, based on the...

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks

Internet of Vehicles IoV systems, while offering significant advancements in transportation efficiency and safety, introduce substantial security vulnerabilities due to their highly interconnected nature. These dynamic systems produce massive amounts of data between vehicles, infrastructure, and...

Network Attack Traffic Detection with Hybrid Quantum-Enhanced Convolution Neural Network

The emerging paradigm of Quantum Machine Learning QML combines features of quantum computing and machine learning ML. QML enables the generation and recognition of statistical data patterns that classical computers and classical ML methods struggle to effectively execute. QML utilizes quantum...

Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings

The rapid evolution of malware variants requires robust classification methods to enhance cybersecurity. While Large Language Models LLMs offer potential for generating malware descriptions to aid family classification, their utility is limited by semantic embedding overlaps and misalignment with...

Trace Gadgets: Minimizing Code Context for Machine Learning-Based Vulnerability Prediction

As the number of web applications and API endpoints exposed to the Internet continues to grow, so does the number of exploitable vulnerabilities. Manually identifying such vulnerabilities is tedious. Meanwhile, static security scanners tend to produce many false positives. While machine...

CVE-2024-7432

The Unseen Blog theme for WordPress is vulnerable to PHP Object Injection in all versions up to, and including, 1.0.0 via deserialization of untrusted input. This makes it possible for authenticated attackers, with Contributor-level access and above, to inject a PHP Object. No known POP chain is...

CVE-2024-7432

The Unseen Blog theme for WordPress is vulnerable to PHP Object Injection in all versions up to, and including, 1.0.0 via deserialization of untrusted input. This makes it possible for authenticated attackers, with Contributor-level access and above, to inject a PHP Object. No known POP chain is...

CVE-2024-7432

The Unseen Blog theme for WordPress is vulnerable to PHP Object Injection in all versions up to, and including, 1.0.0 via deserialization of untrusted input. This makes it possible for authenticated attackers, with Contributor-level access and above, to inject a PHP Object. No known POP chain is...

CVE-2024-7432 Unseen Blog <= 1.0.0 - Authenticated (Contributor+) PHP Object Injection

The Unseen Blog theme for WordPress is vulnerable to PHP Object Injection in all versions up to, and including, 1.0.0 via deserialization of untrusted input. This makes it possible for authenticated attackers, with Contributor-level access and above, to inject a PHP Object. No known POP chain is...

CVE-2024-7432 Unseen Blog <= 1.0.0 - Authenticated (Contributor+) PHP Object Injection

The Unseen Blog theme for WordPress is vulnerable to PHP Object Injection in all versions up to, and including, 1.0.0 via deserialization of untrusted input. This makes it possible for authenticated attackers, with Contributor-level access and above, to inject a PHP Object. No known POP chain is...