arXiv Machine Learning
Aug 11
The Range Shrinks, the Threat Remains: Re-evaluating LLM Package Hallucinations on the 2026 Frontier-Model Cohort
arXiv:2605. 17062v3 Announce Type: replace-cross Abstract: Spracklen et al.
Evaluating Large Language Models for Symbolic Security Protocol Analysis
arXiv:2607. 20712v1 Announce Type: cross Abstract: Security protocol verification relies on formal tools such as ProVerif and OFMC.
Related stories
arXiv AI
Jun 3
Testing LLM Arithmetic Reasoning Generalization with Automatic Numeric-Remapping Attacks
arXiv:2606. 03606v1 Announce Type: cross Abstract: Large language models achieve strong performance on arithmetic reasoning benchmarks, and one common response to arithmetic brittleness is to delegate computation to code.
Hugging Face Trending Papers
Jun 2
Testing LLM Arithmetic Reasoning Generalization with Automatic Numeric-Remapping Attacks
Large language models achieve strong performance on arithmetic reasoning benchmarks, and one common response to arithmetic brittleness is to delegate computation to code. Yet models are still often used in settings where they must reason directly from natural language, and trustworthy models should solve small-number arithmetic word problems without external tools.
arXiv AI
Jul 23
Small, Free, and Effective: Orchestrating Open-Weight Small Language Models to Outperform Single LLM for Malware Analysis
arXiv:2607. 20216v1 Announce Type: cross Abstract: Malware analysis demands rapid interpretation of complex detonation reports spanning filesystem, network, and process behaviours.
arXiv Machine Learning
Jun 4
Bypassing Prompt Guards in Production with Controlled-Release Prompting
arXiv:2510. 01529v3 Announce Type: replace Abstract: Ball et al.
arXiv AI
Aug 11
Evaluating Jailbreaking Vulnerabilities in LLMs Deployed as Assistants for Smart Grid Operations: A Benchmark Against NERC Standards
arXiv:2604. 23341v3 Announce Type: replace-cross Abstract: The deployment of Large Language Models (LLMs) as assistants in electric grid operations promises to streamline compliance and decision-making but exposes new vulnerabilities to prompt-based adversarial attacks.
arXiv Machine Learning
Jul 30
ToxScreen: Detecting Whether an LLM Has Been Poisoned
arXiv:2607. 26849v1 Announce Type: cross Abstract: As large language models (LLMs) are deployed in high-stakes domains, adversaries may poison training data to implant backdoors: hidden triggers that covertly manipulate model behavior at inference time.
arXiv AI
Jun 29
Mitigating LLM-based p-Hacking by Preregistering for the Next LLM
arXiv:2606. 27687v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate, classify, and annotate data whose outputs feed downstream hypothesis tests.
arXiv AI
Jul 8
Unicode TAG-Block Concealment of Tool-Metadata Payloads in the Model Context Protocol: An Approval-View Fidelity Gap Across Three Independent Server Implementations
arXiv:2607. 05744v1 Announce Type: cross Abstract: The Model Context Protocol (MCP) is the dominant way coding agents discover and invoke external tools.
arXiv Machine Learning
Jul 1
An Empirical Study of Security Calibration in Large Language Models for Code
arXiv:2606. 31159v1 Announce Type: cross Abstract: Large Language Models (LLMs) are rapidly transforming software development, yet their use in security-critical contexts raises a key question: do models know when their generated code is insecure?
arXiv AI
Aug 7
CREBench: Evaluating Large Language Models in Cryptographic Binary Reverse Engineering
arXiv:2604. 03750v2 Announce Type: replace-cross Abstract: Reverse engineering (RE) is central to software security, particularly for cryptographic programs that handle sensitive data and are highly prone to vulnerabilities.
arXiv AI
Jun 11
Are Frontier LLMs Ready for Cybersecurity? Evidence for Vertical Foundation Models from Dual-Mode Vulnerability Benchmarks
arXiv:2605. 23243v2 Announce Type: replace-cross Abstract: We evaluate whether frontier LLMs are ready for cybersecurity through a dual-mode benchmark: white-box function-level vulnerability detection (VulnLLM-R, across C/Java/Python) and black-box web application security testing (five production-style applications with 118 ground-truth vulnerabilities across 20+ CWE families, which we will open-source).