Wardstone Launches AI Security API for Prompt Injection and Jailbreak Detection
Key Takeaways
- ▸Wardstone offers model-agnostic LLM security with sub-30ms latency, protecting applications against prompt injection, jailbreaks, harmful content, and data leakage
- ▸The API provides bidirectional scanning—protecting both user inputs and model outputs—and works with 30+ LLM providers including OpenAI, Anthropic, Google, and Meta
- ▸Transparent, usage-based pricing model starting free (10,000 calls/month) with no credit card required, addressing growing demand for guardrails in production AI systems
Summary
Wardstone has introduced an LLM security API designed to protect AI applications from prompt injection attacks, jailbreaks, harmful content, and data leakage. The platform functions as an AI firewall that works model-agnostic, supporting OpenAI's GPT, Anthropic's Claude, Google Gemini, Meta's Llama, and dozens of other LLM providers through a unified REST API and SDKs.
The service operates with sub-30ms latency and offers bidirectional scanning—analyzing user inputs before processing and filtering model outputs before delivery. Wardstone detects four primary threat categories: prompt attacks, harmful content, PII leakage, and suspicious URLs, returning risk bands by default with optional raw scores for paid tiers.
Pricing follows a straightforward model with a free tier (10,000 API calls/month), a Pro tier ($0.50 per 1,000 overage calls), and Enterprise options with unlimited calls, dedicated support, and compliance features. The platform requires no credit card for free tier access and integrates seamlessly into existing application stacks via drop-in REST APIs and language-specific SDKs for Python, TypeScript, and other languages.
Editorial Opinion
Wardstone addresses a critical pain point in AI deployment: the need for robust, low-latency security guardrails that don't compromise application performance. By offering model-agnostic protection with sub-30ms overhead, the platform removes a key friction point for enterprises integrating LLMs into production workflows. However, the effectiveness of such detection APIs ultimately depends on the sophistication of their threat models—prompt injection and jailbreak techniques evolve rapidly, and success will require continuous model updates and adversarial testing.
