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The author built AgentPaySec, a security testing platform for AI agents with financial authority, tested it on a simulated payment agent, found vulnerabilities, and is seeking community feedback on attack scenarios.
HarnessRisk is a lifecycle-oriented benchmark for evaluating agent harness safety, revealing configuration vulnerabilities and detection gaps that allow high attack success rates while maintaining utility.
This article discusses creating benchmarks for more programming languages to evaluate the cybersecurity capabilities of large language models, and announces the release of JSEF v1.3.0, a Java security teaching framework and benchmark for measuring the vulnerability detection capabilities of SAST tools and LLMs.
Antitech is offering free early-access security assessments for AI agents, testing against attack vectors like prompt injection, tool abuse, and data leakage, providing a vulnerability report and discounts for participants.
RedBench introduces a universal dataset aggregating 37 benchmark datasets with 29,362 samples across 22 risk categories and 19 domains to enable standardized and comprehensive red teaming evaluation of large language models. The work addresses inconsistencies in existing red teaming datasets and provides baselines, evaluation code, and open-source resources for assessing LLM robustness against adversarial prompts.