Picklescan has a missing detection when calling built-in python idlelib.autocomplete.AutoComplete.get_entity

Summary Using idlelib.autocomplete.AutoComplete.getentity, which is a built-in python library function to execute remote pickle file. Details The attack payload executes in the following steps: First, the attacker craft the payload by calling to idlelib.autocomplete.AutoComplete.getentity functio...

Picklescan has a missing detection when calling built-in python profile.Profile.run

Summary Using profile.Profile.run, which is a built-in python library function to execute remote pickle file. Details The attack payload executes in the following steps: First, the attacker craft the payload by calling to profile.Profile.run function in reduce method Then when the victim after...

$AutoGuardX$: a Comprehensive Cybersecurity Framework for Connected Vehicles

The rapid integration of Internet of Things IoT and interconnected systems in modern vehicles not only introduced a new era of convenience, automation, and connected vehicles but also elevated their exposure to sophisticated cyber threats. This is especially evident in US and Canada, where...

A Comprehensive Review of Denial of Wallet Attacks in Serverless Architectures

The Denial of Wallet DoW attack poses a unique and growing threat to serverless architectures that rely on Function-as-a-Service FaaS models, exploiting the cost structure of pay-as-you-go billing to financially burden application owners. Unlike traditional Denial of Service DoS attacks, which ai...

Picklescan missing detection when calling pytorch function torch.jit.unsupported_tensor_ops.execWrapper

Summary Using torch.jit.unsupportedtensorops.execWrapper function, which is a pytorch library function to execute remote pickle file. Details The attack payload executes in the following steps: First, the attacker craft the payload by calling to torch.jit.unsupportedtensorops.execWrapper function...

When Machine Learning Meets Vulnerability Discovery: Challenges and Lessons Learned

In recent years, machine learning has demonstrated impressive results in various fields, including software vulnerability detection. Nonetheless, using machine learning to identify software vulnerabilities presents new challenges, especially regarding the scale of data involved, which was not a...

DDoS Attacks in Cloud Computing: Detection and Prevention

DDoS attacks are one of the most prevalent and harmful cybersecurity threats faced by organizations and individuals today. In recent years, the complexity and frequency of DDoS attacks have increased significantly, making it challenging to detect and mitigate them effectively. The study analyzes...

Activate Me!: Designing Efficient Activation Functions for Privacy-Preserving Machine Learning with Fully Homomorphic Encryption

The growing adoption of machine learning in sensitive areas such as healthcare and defense introduces significant privacy and security challenges. These domains demand robust data protection, as models depend on large volumes of sensitive information for both training and inference. Fully...

Machine Learning-Based AES Key Recovery Via Side-Channel Analysis on the ASCAD Dataset

Cryptographic algorithms like AES and RSA are widely used and they are mathematically robust and almost unbreakable but its implementation on physical devices often leak information through side channels, such as electromagnetic EM emissions, potentially compromising said theoretically secure...

Enhancing GraphQL Security by Detecting Malicious Queries Using Large Language Models, Sentence Transformers, and Convolutional Neural Networks

GraphQL's flexibility, while beneficial for efficient data fetching, introduces unique security vulnerabilities that traditional API security mechanisms often fail to address. Malicious GraphQL queries can exploit the language's dynamic nature, leading to denial-of-service attacks, data...

Demystifying the Role of Rule-Based Detection in AI Systems for Windows Malware Detection

Malware detection increasingly relies on AI systems that integrate signature-based detection with machine learning. However, these components are typically developed and combined in isolation, missing opportunities to reduce data complexity and strengthen defenses against adversarial EXEmples,...

Enhance the Machine Learning Algorithm Performance in Phishing Detection with Keyword Features

Recently, we can observe a significant increase of the phishing attacks in the Internet. In a typical phishing attack, the attacker sets up a malicious website that looks similar to the legitimate website in order to obtain the end-users' information. This may cause the leakage of the sensitive...

Designing with Deception: ML- and Covert Gate-Enhanced Camouflaging to Thwart IC Reverse Engineering

Integrated circuits ICs are essential to modern electronic systems, yet they face significant risks from physical reverse engineering RE attacks that compromise intellectual property IP and overall system security. While IC camouflage techniques have emerged to mitigate these risks, existing...

Generative AI for Critical Infrastructure in Smart Grids: a Unified Framework for Synthetic Data Generation and Anomaly Detection

In digital substations, security events pose significant challenges to the sustained operation of power systems. To mitigate these challenges, the implementation of robust defense strategies is critically important. A thorough process of anomaly identification and detection in information and...

Security Bulletin: Multiple vulnerabilities in IBM Business Automation Workflow Machine Learning Server are addressed with 24.0.0-IF006

Summary In addition to updates to operating system level packages, IBM Business Automation Workflow Machine Learning Server 24.0.0-IF006 addresses the following vulnerabilities. Vulnerability Details CVEID:CVE-2024-47081 DESCRIPTION: Requests is a HTTP library. Due to a URL parsing issue, Request...

Measuring the Carbon Footprint of Cryptographic Privacy-Enhancing Technologies

Privacy-enhancing technologies PETs have attracted significant attention in response to privacy regulations, driving the development of applications that prioritize user data protection. At the same time, the information and communication technology ICT sector faces growing pressure to reduce its...

Leveraging Machine Learning for Botnet Attack Detection in Edge-Computing Assisted IoT Networks

The increase of IoT devices, driven by advancements in hardware technologies, has led to widespread deployment in large-scale networks that process massive amounts of data daily. However, the reliance on Edge Computing to manage these devices has introduced significant security vulnerabilities, a...

CVE-2025-54430

dedupe is a python library that uses machine learning to perform fuzzy matching, deduplication and entity resolution quickly on structured data. Before commit 3f61e79, a critical severity vulnerability has been identified within the .github/workflows/benchmark-bot.yml workflow, where a issuecomme...

PT-2025-31382 · Dedupe · Dedupe

Name of the Vulnerable Software and Affected Versions: dedupe versions prior to commit 3f61e79 Description: dedupe is a Python library used for fuzzy matching, deduplication, and entity resolution on structured data. A critical severity issue exists in the .github/workflows/benchmark-bot.yml...

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection

Despite outstanding results, machine learning-based Android malware detection models struggle with concept drift, where rapidly evolving malware characteristics degrade model effectiveness. This study examines the impact of concept drift on Android malware detection, evaluating two datasets and...