CVE-2026-31229

The Adversarial Robustness Toolbox ART thru 1.20.1 contains an insecure deserialization vulnerability CWE-502 in its Kubeflow component's model loading functionality. When loading model weights from a file e.g., model.pt during robustness evaluation, the code uses torch.load without the...

PT-2026-40117

The Adversarial Robustness Toolbox ART thru 1.20.1 contains a command-line argument injection vulnerability in its Kubeflow component robustness evaluation fgsm pytorch.py. The script uses the unsafe eval function to parse string values provided via the --clip values and --input shape command-lin...

Do Androids Dream of Breaking the Game? Systematically Auditing AI Agent Benchmarks with BenchJack

Agent benchmarks have become the de facto measure of frontier AI competence, guiding model selection, investment, and deployment. However, reward hacking, where agents maximize a score without performing the intended task, emerges spontaneously in frontier models without overfitting. We argue tha...

CVE-2026-31228

The Adversarial Robustness Toolbox ART thru 1.20.1 contains a remote code execution vulnerability in its Kubeflow component. The robustness evaluation function for PyTorch models uses the unsafe eval function to dynamically evaluate user-supplied strings for the LossFn and Optimizer parameters...

Adversarial Robustness Toolbox 安全漏洞

Adversarial Robustness Toolbox is an open-source machine learning security defense and evaluation tool developed by Trusted-AI. Versions of Adversarial Robustness Toolbox 1.20.1 and earlier contained security vulnerabilities. These vulnerabilities stemmed from the model loading function in the...

CVE-2026-31229

The ART (Adversarial Robustness Toolbox) package up to v1.20.1 contains an insecure deserialization vulnerability in its Kubeflow component’s model loading path. Loading model weights (e.g., model.pt) uses torch.load() without weights_only=True, allowing arbitrary Python object deserialization vi...

PT-2026-40116

The Adversarial Robustness Toolbox ART thru 1.20.1 contains an insecure deserialization vulnerability CWE-502 in its Kubeflow component's model loading functionality. When loading model weights from a file e.g., model.pt during robustness evaluation, the code uses torch.load without the...

CVE-2026-31228

The Adversarial Robustness Toolbox ART thru 1.20.1 contains a remote code execution vulnerability in its Kubeflow component. The robustness evaluation function for PyTorch models uses the unsafe eval function to dynamically evaluate user-supplied strings for the LossFn and Optimizer parameters...

Adversarial Robustness Toolbox 安全漏洞

Adversarial Robustness Toolbox is an open-source machine learning security defense and evaluation tool developed by Trusted-AI. Versions of Adversarial Robustness Toolbox 1.20.1 and earlier contained security vulnerabilities. These vulnerabilities stemmed from the robustnessevaluationfgsmpytorch....

CVE-2026-31230

The CVE-2026-31230 vulnerability concerns the Adversarial Robustness Toolbox (ART) up to v1.20.1, specifically in its Kubeflow component (robustness_evaluation_fgsm_pytorch.py). The issue arises from using unsafe eval() to parse string values passed via --clip_values and --input_shape, enabling a...

Smart Contract Security beyond Detection

Smart contract security has progressed from vulnerability detection toward a broader research agenda that includes semantic reasoning, automated repair, adversarial robustness, and real-time exploit detection. This paper develops a capstone-oriented research narrative around four directions:...

Enhancing Adversarial Robustness in Network Intrusion Detection: A Layer-Wise Adaptive Regularization Approach

The new wave of adversarial attacks that utilize gradient-related vulnerabilities in neural network-based classifiers makes Network Intrusion Detection Systems more open to such threats. Although state-of-the-art adversarial training methods have shown promising results in producing more robust...

AI-Driven Security Alert Screening and Alert Fatigue Mitigation in Security Operations Centers: A Comprehensive Survey

Security alert screening is the downstream task of filtering, prioritizing, correlating, and contextualizing alerts for analyst attention in Security Operations Centers. This survey reviews artificial-intelligence-driven alert screening and alert-fatigue mitigation from 2015 to 2026. We synthesiz...

Beyond the Wrapper: Identifying Artifact Reliance in Static Malware Classifiers Using TRUSTEE

Modern cybersecurity relies heavily on static machine-learning-based malware classifiers. However, transformations such as packing and other non-semantic modifications applied to executable files limit their reliability. Malware classifiers often learn these unnecessary artifacts rather than the...

CVE-2026-43212

The CVE-2026-43212 entries involve the Linux kernel on LoongArch where cpumask_of_node() failed to handle NUMA_NO_NODE, which is a valid index. The root cause is an insufficient check in the arch-specific cpumask_of_node() implementation, leading to potential instability or incorrect behavior if ...

AoI-Guided Client Selection for Robust and Timely Federated Intrusion Detection in Cloud-Edge Security Analytics

Federated learning FL is attractive for cloud-edge intrusion detection because it enables collaborative training over distributed telemetry without centralizing raw logs. In production security analytics pipelines, however, only a subset of clients participates in each round, and heterogeneous...

AsmRAG: LLM-Driven Malware Detection by Retrieving Functionally Similar Assembly Code

Deep learning malware detectors achieve high classification accuracy but suffer from severe interpretability limitations, typically returning probabilistic verdicts that lack forensic context. We introduce AsmRAG, a framework performing malware analysis through Assembly-Level Retrieval-Augmented...

Keras 3.13.0 HDF5 Shape Fuzzing for Robustness Testing

This script performs fuzz testing against Keras version 3.13.0 on randomly generated tensor shapes using NumPy and HDF5 to evaluate stability and error handling in file creation workflows...

Towards Certified Malware Detection: Provable Guarantees against Evasion Attacks

Machine learning-based static malware detectors remain vulnerable to adversarial evasion techniques, such as metamorphic engine mutations. To address this vulnerability, we propose a certifiably robust malware detection framework based on randomized smoothing through feature ablation and targeted...

DNG File Fuzzer for Robustness

This Python script is a mutation-based fuzzing tool designed to test the robustness of DNG Digital Negative / TIFF-based file parsers by generating large numbers of corrupted or semi-valid image files. It works by starting from a minimal valid DNG structure, then applying random mutations to...