CVE-2026-3962

A vulnerability was identified in Jcharis Machine-Learning-Web-Apps up to a6996b634d98ccec4701ac8934016e8175b60eb5. The impacted element is the function rendertemplate of the file Machine-Learning-Web-Apps-master/Build-n-Deploy-Flask-App-with-Waypoint/app/app.py of the component Jinja2 Template...

CVE-2026-3962

The CVE-2026-3962 entry affects Jcharis Machine-Learning-Web-Apps (up to a6996b634d98ccec4701ac8934016e8175b60eb5) where the render_template function in Machine-Learning-Web-Apps-master/Build-n-Deploy-Flask-App-with-Waypoint/app/app.py under the Jinja2 Template Handler is vulnerable to cross-site...

CVE-2026-3962 Jcharis Machine-Learning-Web-Apps Jinja2 Template app.py render_template cross site scripting

A vulnerability was identified in Jcharis Machine-Learning-Web-Apps up to a6996b634d98ccec4701ac8934016e8175b60eb5. The impacted element is the function rendertemplate of the file Machine-Learning-Web-Apps-master/Build-n-Deploy-Flask-App-with-Waypoint/app/app.py of the component Jinja2 Template...

CVE-2026-3920

Out of bounds memory access in WebML in Google Chrome prior to 146.0.7680.71 allowed a remote attacker to potentially exploit heap corruption via a crafted HTML page. Chromium security severity: High...

Machine-Learning-Web-Apps 代码注入漏洞

Machine-Learning-Web-Apps is a machine learning web application development framework developed by JCharis Jesse. There is a code injection vulnerability in Machine-Learning-Web-Apps, which stems from an incorrect operation on the rendertemplate function in the Jinja2 Template Handler component o...

PT-2026-24891

A vulnerability was identified in Jcharis Machine-Learning-Web-Apps up to a6996b634d98ccec4701ac8934016e8175b60eb5. The impacted element is the function render template of the file Machine-Learning-Web-Apps-master/Build-n-Deploy-Flask-App-with-Waypoint/app/app.py of the component Jinja2 Template...

CVE-2026-0847

A vulnerability in NLTK versions up to and including 3.9.2 allows arbitrary file read via path traversal in multiple CorpusReader classes, including WordListCorpusReader, TaggedCorpusReader, and BracketParseCorpusReader. These classes fail to properly sanitize or validate file paths, enabling...

CVE-2026-0847

A vulnerability in NLTK versions up to and including 3.9.2 allows arbitrary file read via path traversal in multiple CorpusReader classes, including WordListCorpusReader, TaggedCorpusReader, and BracketParseCorpusReader. These classes fail to properly sanitize or validate file paths, enabling...

Gravity Falls: A Comparative Analysis of Domain-Generation Algorithm (DGA) Detection Methods for Mobile Device Spearphishing

Mobile devices are frequent targets of eCrime threat actors through SMS spearphishing smishing links that leverage Domain Generation Algorithms DGA to rotate hostile infrastructure. Despite this, DGA research and evaluation largely emphasize malware C2 and email phishing datasets, leaving limited...

AMDS: Attack-Aware Multi-Stage Defense System for Network Intrusion Detection with Two-Stage Adaptive Weight Learning

Machine learning based network intrusion detection systems are vulnerable to adversarial attacks that degrade classification performance under both gradient-based and distribution shift threat models. Existing defenses typically apply uniform detection strategies, which may not account for...

ai-security-toolkit

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SafePickle: Robust and Generic ML Detection of Malicious Pickle-Based ML Models

Model repositories such as Hugging Face increasingly distribute machine learning artifacts serialized with Python's pickle format, exposing users to remote code execution RCE risks during model loading. Recent defenses, such as PickleBall, rely on per-library policy synthesis that requires comple...

Detecting PowerShell-Based Fileless Cryptojacking Attacks Using Machine Learning

With the emergence of remote code execution RCE vulnerabilities in ubiquitous libraries and advanced social engineering techniques, threat actors have started conducting widespread fileless cryptojacking attacks. These attacks have become effective with stealthy techniques based on PowerShell-bas...

Security Bulletin: IBM Watson Machine Learning Accelerator for IBM Cloud Pak for Data is affected by multiple vulnerabilities.

Summary Multiple vulnerabilities were addressed in IBM Watson Machine Learning Accelerator for IBM Cloud Pak for Data. Follow the instructions in this document to address them. Vulnerability Details CVEID:CVE-2021-38291 DESCRIPTION: FFmpeg version git commit de8e6e67e7523e48bb27ac224a0b446df05e16...

One RNG to Rule Them All: How Randomness Becomes an Attack Vector in Machine Learning

Machine learning relies on randomness as a fundamental component in various steps such as data sampling, data augmentation, weight initialization, and optimization. Most machine learning frameworks use pseudorandom number generators as the source of randomness. However, variations in design choic...

Empirical Evaluation of SMOTE in Android Malware Detection with Machine Learning: Challenges and Performance in CICMalDroid 2020

Malware, malicious software designed to damage computer systems and perpetrate scams, is proliferating at an alarming rate, with thousands of new threats emerging daily. Android devices, prevalent in smartphones, smartwatches, tablets, and IoTs, represent a vast attack surface, making malware...

Evasion of IoT Malware Detection Via Dummy Code Injection

The Internet of Things IoT has revolutionized connectivity by linking billions of devices worldwide. However, this rapid expansion has also introduced severe security vulnerabilities, making IoT devices attractive targets for malware such as the Mirai botnet. Power side-channel analysis has...

Hardware-Triggered Backdoors

Machine learning models are routinely deployed on a wide range of computing hardware. Although such hardware is typically expected to produce identical results, differences in its design can lead to small numerical variations during inference. In this work, we show that these variations can be...

A Systematic Literature Review on LLM Defenses against Prompt Injection and Jailbreaking: Expanding NIST Taxonomy

The rapid advancement and widespread adoption of generative artificial intelligence GenAI and large language models LLMs has been accompanied by the emergence of new security vulnerabilities and challenges, such as jailbreaking and other prompt injection attacks. These maliciously crafted inputs...

CVE-2025-15469

Issue summary: The 'openssl dgst' command-line tool silently truncates input data to 16MB when using one-shot signing algorithms and reports success instead of an error. Impact summary: A user signing or verifying files larger than 16MB with one-shot algorithms such as Ed25519, Ed448, or ML-DSA m...