
SecraSecurity layer that shields AI agents from attacks, prompt injection, and data leaks.
Overview
Key features
- Prompt injection and jailbreak detection
- Sensitive data and PII filtering
- Agent activity monitoring and logging
- Tool-use and output policy enforcement
- Alerts for suspicious behavior
- Integration with common LLM providers
Pricing
- Model
- Free
- Category
- AI security
- Rating
- 4.5 / 5 (4)
Use cases
Block prompt injection in customer-facing agents
Filter incoming user messages and tool outputs to detect jailbreaks and injection attempts before they reach the LLM, protecting public-facing chatbots from manipulation.
Prevent PII and credential leaks
Scan agent inputs and outputs for sensitive data like PII, secrets, or proprietary content to stop accidental exposure in logs, responses, or downstream tools.
Enforce tool-use policies for autonomous agents
Apply guardrails on which tools an agent can invoke and under what conditions, preventing unauthorized actions during multi-step or autonomous workflows.
Audit agent behavior in production
Give security teams logs, alerts, and visibility into how AI agents are being used and abused, enabling incident response and compliance reviews.
Pros & Cons
Pros
- Focused on emerging AI agent threats
- Helps prevent data leaks and PII exposure
- Provides visibility into agent behavior
- Works as a layer over existing models
Cons
- Adds latency to agent calls
- Requires integration work to deploy
- Effectiveness depends on threat coverage
Reviews
Average from 4 ratings.
Sign in to leave a review.
Does the job
Pretty happy overall. Sensitive data and PII filtering just works and focused on emerging AI agent threats. Effectiveness depends on threat coverage can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.
Solid for our team
We rolled this out across the team last quarter and works as a layer over existing models. Agent activity monitoring and logging fits neatly into how we already work, and sensitive data and PII filtering removed a step we used to do by hand. Adds latency to agent calls, which is the main caveat, but it has held up under daily use.
Does the job
Pretty happy overall. Alerts for suspicious behavior just works and provides visibility into agent behavior. but no dealbreakers — I'd recommend it to a friend without hesitating.
Does the job
Pretty happy overall. Sensitive data and PII filtering just works and focused on emerging AI agent threats. Effectiveness depends on threat coverage can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.
Q&A
What is Secra?
Secra is an AI security infrastructure layer that sits between your AI agents and the LLM. It detects and blocks prompt injection attacks, persona hijacking, and data exfiltration in real time before they reach your model.
Asked by Tomáš Novák · Dec 3, 2025
How does Secra detect prompt injection?
Secra uses a 3-layer detection pipeline: Layer 0 is an Aho-Corasick pre-processor that catches 30+ known injection signatures in under 1ms at zero token cost. Layer 1 applies a regex rule engine for pattern-based threats. Layer 2 uses Groq LLM inference only for ambiguous cases scoring 0.25-0.75.
Asked by Dara Fitzgerald · Nov 9, 2025
Is Secra free to use?
Yes. Secra's Free plan includes 50K tokens per month at no cost, forever. Paid plans start at $15/month for 200K tokens (Developer) and $49/month for 500K tokens with real-time monitoring (Pro).
Asked by Quoc Bui · Oct 19, 2025
Does Secra store my prompts?
No. Secra never stores raw prompts. Only SHA-256 hashes and metadata (scan type, threat score, recommendation, token count) are retained for audit history.
Asked by Noor Siddiqui · Oct 9, 2025
How do I integrate Secra into my AI agent?
Install the SDK with 'pip install secra-sdk' (Python) or 'npm install @secra/sdk' (JavaScript), set your SECRA_API_KEY environment variable, and call client.scan(prompt) before passing input to your LLM. The entire integration takes under 2 minutes.
Asked by Faisal Rahman · Oct 1, 2025
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