Rogue in Production: Stress-Test AI Agents with A2A Red Teaming
4 days ago 高效码农t’s 2 a.m. Slack is screaming. Your customer-support agent just gave a 15-year-old a vape-discount code, the legal team is drafting headlines, and your unit tests are still green. Sound familiar? Traditional QA wasn’t built for conversational, policy-bound, stochastically creative creatures. That’s exactly why Qualifire open-sourced Rogue—an A2A-native red-team that turns written policies into CI/CD gates. Below is the full field manual: install it, abuse it, ship with confidence. 1. The Gap No One Talks About What classic tests check What agents actually break Single-turn intent accuracy Multi-turn memory loss Static prompt answers Policy circumvention Scalar “LLM-as-Judge” score Audit-trail vacuum …
CircleGuardBench: The Missing Link in AI Safety Evaluation Frameworks
5 months ago 高效码农CircleGuardBench: The Definitive Framework for Evaluating AI Safety Systems CircleGuardBench Logo Why Traditional AI Safety Benchmarks Are Falling Short As large language models (LLMs) process billions of daily queries globally, their guardrail systems face unprecedented challenges. While 92% of organizations prioritize AI safety, existing evaluation methods often miss critical real-world factors. Enter CircleGuardBench – the first benchmark combining accuracy, speed, and adversarial resistance into a single actionable metric. The Five-Pillar Evaluation Architecture 1.1 Beyond Basic Accuracy: A Production-Ready Framework Traditional benchmarks focus on static accuracy metrics. CircleGuardBench introduces a dynamic evaluation matrix: Precision Targeting: 17 risk categories mirroring real-world abuse …