Agentic-CLIP-Benchmark

Agentic-CLIP-Benchmark: Zero-Shot Evaluation on CIFAR-10 -green.svg This project implements a robust, automated pipeline for evaluating OpenAI's CLIP ViT-B/32 model on the full CIFAR-10 test set 10,000 images. Developed using an AI Native workflow Trae IDE , it achieves a high-precision zero-shot...

RLCDAlignBench

Just Ask Jev: Reinforcement Learning for Calibrated Decisions as a Zero-Shot Detector of AI Alignment Failures This repository holds RLCDAlignBench and the code behind the paper. The benchmark measures whether a detector can tell when a language model's output is an alignment failure. It has 44...

SAFE

SAFE SAFE performs controlled, repository-aware security assessment of Semgrep and Trivy findings in research artifacts. It supports two independent classification tasks: direct binary prediction SECURITYRELEVANT or NONSECURITY and the detailed multiclass contextual taxonomy three labels — see...

Swiss-Bench 003: Evaluating LLM Reliability and Adversarial Security for Swiss Regulatory Contexts

The deployment of large language models LLMs in Swiss financial and regulatory contexts demands empirical evidence of both production reliability and adversarial security, dimensions not jointly operationalized in existing Swiss-focused evaluation frameworks. This paper introduces Swiss-Bench 003...

CVE-2026-23654

Dependency on vulnerable third-party component in GitHub Repo: zero-shot-scfoundation allows an unauthorized attacker to execute code over a network...

NASimJax: GPU-Accelerated Policy Learning Framework for Penetration Testing

Penetration testing, the practice of simulating cyberattacks to identify vulnerabilities, is a complex sequential decision-making task that is inherently partially observable and features large action spaces. Training reinforcement learning RL policies for this domain faces a fundamental...

The vulnerability of the Zero Shot scFoundation software lies in the presence of a vulnerability in the borrowed component, allowing a perpetrator to execute arbitrary code.

The vulnerability of the Zero Shot scFoundation software is related to the presence of a vulnerability in the borrowed component. Exploiting this vulnerability allows a remote attacker to execute arbitrary code...

EUVD-2026-10577

Dependency on vulnerable third-party component in GitHub Repo: zero-shot-scfoundation allows an unauthorized attacker to execute code over a network...

EUVD-2026-10578

Dependency on vulnerable third-party component in GitHub Repo: zero-shot-scfoundation allows an unauthorized attacker to execute code over a network...

CVE-2026-23654

Dependency on vulnerable third-party component in GitHub Repo: zero-shot-scfoundation allows an unauthorized attacker to execute code over a network...

CVE-2026-23654

Dependency on vulnerable third-party component in GitHub Repo: zero-shot-scfoundation allows an unauthorized attacker to execute code over a network...

CVE-2026-23654 GitHub: Zero Shot SCFoundation Remote Code Execution Vulnerability

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CVE-2026-23654 GitHub: Zero Shot SCFoundation Remote Code Execution Vulnerability

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Microsoft GitHub Repo: Zero Shot scFoundation 安全漏洞

Microsoft GitHub Repo: Zero Shot scFoundation is a biological information research code base owned by Microsoft Corporation. There are security vulnerabilities present in Microsoft GitHub Repo: Zero Shot scFoundation. Attackers can exploit these vulnerabilities to execute code remotely...

CVE-2026-23654: Dependency on Vulnerable Third-Party Component

Dependency on vulnerable third-party component in GitHub Repo: zero-shot-scfoundation allows an unauthorized attacker to execute code over a network...

SecureRAG-RTL: A Retrieval-Augmented, Multi-Agent, Zero-Shot LLM-Driven Framework for Hardware Vulnerability Detection

Large language models LLMs have shown remarkable capabilities in natural language processing tasks, yet their application in hardware security verification remains limited due to scarcity of publicly available hardware description language HDL datasets. This knowledge gap constrains LLM performan...

MultiVer: Zero-Shot Multi-Agent Vulnerability Detection

We present MultiVer, a zero-shot multi-agent system for vulnerability detection that achieves state-of-the-art recall without fine-tuning. A four-agent ensemble security, correctness, performance, style with union voting achieves 82.7% recall on PyVul, exceeding fine-tuned GPT-3.5 81.3% by 1.4...

LLM-FS: Zero-Shot Feature Selection for Effective and Interpretable Malware Detection

Feature selection FS remains essential for building accurate and interpretable detection models, particularly in high-dimensional malware datasets. Conventional FS methods such as Extra Trees, Variance Threshold, Tree-based models, Chi-Squared tests, ANOVA, Random Selection, and Sequential...

Benchmarking Large Language Models for Zero-Shot and Few-Shot Phishing URL Detection

The Uniform Resource Locator URL, introduced in a connectivity-first era to define access and locate resources, remains historically limited, lacking future-proof mechanisms for security, trust, or resilience against fraud and abuse, despite the introduction of reactive protections like HTTPS...

Lightweight LLMs for Network Attack Detection in IoT Networks

The rapid growth of Internet of Things IoT devices has increased the scale and diversity of cyberattacks, exposing limitations in traditional intrusion detection systems. Classical machine learning ML models such as Random Forest and Support Vector Machine perform well on known attacks but requir...