General Analysis is open sourcing the GA Guard models — the first family of long context safety LLMs that have been protecting enterprise AI deployments for the past year.
The lineup:
- GA Guard – our default, up to 15x faster than cloud providers.
- GA Guard Lite – ultra-fast (up to 25x faster) with minimal hardware.
- GA Guard Thinking – hardened for high-risk domains.
We’re also releasing two open benchmarks: GA Long Context Bench for long-context moderation and GA Jailbreak Bench for classifying jailbreak attempts.
GA Guards are trained to detect 7 categories of harmful requests and outputs including complicated jailbreak attempts and prompt injections, PII and sensitive data, illegal requests, hate, sexual content, prompt injections, violence/self-harm, and misinformation.
By the Numbers
LLMs and agents are increasingly processing long inputs like PDFs and web results, where attackers can easily embed highly complex prompt injections, hidden instructions, or obfuscated payloads. Most existing guardrails aren’t designed to moderate such inputs (Azure tops out at ~2.5k tokens and AWS around ~6k). GA Guards are the first moderation models trained on agentic sequences up to 256k tokens.
We evaluated GA Guards on public moderation suites, our adversarial GA Jailbreak Bench, and the new GA Long-Context Bench. Across all three, our models consistently outperform major cloud guardrails and even surpass GPT-5 (when prompted to act as a guardrail) while running far faster.
On GA Long-Context Bench for example, GA Guard Thinking scores 0.893 F1, GA Guard 0.891, and GA Guard Lite 0.885. Cloud baselines struggle: Vertex reaches 0.560, AWS misclassifies nearly all inputs with a 1.0 false-positive rate, and Azure records just 0.046 F1 (see the full results on our website).
GA Guards deliver almost 400× faster performance than GPT-5 (Lite: 0.016s vs 11.275s; Base: 0.029s) and 15–25× faster than cloud guardrails.
All three models are available on Hugging Face — try them out and see how they perform in your own workflows.