AI Developers Have a Security Blind Spot
You're building an AI-powered app. Maybe it's a customer support chatbot, a code assistant, a document analyzer, or an autonomous agent that can take actions. You've got your OpenAI or Anthropic API key, your vector database for RAG, and your carefully crafted system prompt.
What you probably don't have: any defense against prompt injection, jailbreaking, data poisoning, or agent hijacking. Most AI developers treat security as "don't expose the API key" and call it done. But the API key isn't the attack surface — the prompt is.
Threats Specific to AI Applications
AI apps face an entirely new category of attacks that traditional security tools don't understand.
Prompt Injection (65+ Patterns Detected)
1-SEC's LLM Firewall catches direct injection ("ignore previous instructions"), indirect injection (malicious content in documents your RAG pipeline ingests), encoding evasion (Base64, ROT13, Unicode homoglyphs), and multi-turn attacks that slowly shift context across conversations. All detection is rule-based — zero LLM calls, microsecond latency, deterministic results.
Jailbreak Detection
DAN prompts, FlipAttack, many-shot jailbreaking, time bandit attacks, and dozens of other jailbreak techniques are detected and blocked. The LLM Firewall maintains a continuously updated pattern library that catches both known jailbreaks and structural variants that follow the same patterns.
Agent Hijacking and Tool Abuse
If your AI agent can browse the web, execute code, or call APIs, it can be hijacked via prompt injection to do those things for an attacker. 1-SEC's AI Agent Containment module enforces action sandboxing — restricting what tools an agent can use, what resources it can access, and what sequences of actions are permitted. An agent that suddenly starts accessing files outside its scope or making API calls it's never made before gets flagged immediately.
RAG Pipeline Poisoning
Your RAG pipeline ingests documents, web pages, or database records into a vector store. If an attacker can inject content into those sources, they can poison your AI's knowledge base. 1-SEC's Data Poisoning Guard validates RAG pipeline inputs for adversarial content, monitors for unexpected changes in your training data, and detects model drift that could indicate poisoning.
How AI Developers Integrate 1-SEC
Two approaches depending on your architecture:
Sidecar deployment: Run 1-SEC alongside your AI application server. It monitors all traffic including LLM API calls, user inputs, and agent actions. Zero code changes to your app.
API integration: Use 1-SEC's REST API to scan inputs before they reach your LLM. POST user messages to /api/v1/events and check the response for injection or jailbreak detections. This gives you programmatic control over what happens when an attack is detected.
For Python developers using LangChain, LlamaIndex, or custom pipelines: 1-SEC's scan command accepts stdin, so you can pipe prompts through it as a pre-processing step:
echo "$user_input" | 1sec scan --module llm_firewall --type prompt_injection
The exit code tells you whether the input is clean (0) or flagged (1). Simple enough to integrate into any pipeline.
Protecting Your API Budget
Token budget exhaustion is a real attack. An attacker who can trigger expensive LLM calls — long prompts, chain-of-thought reasoning, tool-use loops — can drain your API credits in hours. 1-SEC's LLM Firewall includes token budget monitoring that tracks usage per session and per hour, alerting when consumption patterns suggest abuse.
For startups burning through OpenAI credits, this alone can save thousands of dollars. A single prompt injection that triggers an infinite agent loop can cost more than a month of normal usage.