Your AI agent passed every security scan before deployment. Then it encountered a malicious prompt in production and everything changed.
Why isn't pre-deployment scanning enough?
Static security analysis catches known vulnerabilities in your code and dependencies. But LLM applications face threats that don't exist until runtime:
- Prompt injection — malicious instructions embedded in user inputs that manipulate agent behavior
- Dynamic content — threats hidden in data your agent fetches from external sources
- Behavioral anomalies — patterns that only emerge through actual usage (unusual API calls, unexpected file access)
- Cost attacks — prompts designed to trigger expensive model calls or infinite loops
An agent can be perfectly secure in isolation but compromised the moment it processes untrusted input. Runtime protection bridges this gap.
How does runtime protection work?
Runtime protection sits between your agent and the outside world, inspecting traffic and behavior as it happens.
Traffic inspection: Monitors API calls to LLM providers (OpenAI, Anthropic, etc.) for suspicious patterns. Flags unusual request volumes, unexpected model switching, or content that matches known attack signatures.
Input scanning: Analyzes incoming prompts and data before they reach your agent's core logic. Detects prompt injection attempts, encoded payloads, and manipulation techniques in real-time.
Behavioral monitoring: Tracks what your agent actually does — files accessed, commands executed, external services called. Alerts when behavior deviates from expected patterns.
Cost controls: Monitors token usage and API spend continuously. Triggers alerts or circuit breakers before costs spiral.
How do I add runtime protection to my LLM application?
1. Implement a traffic proxy.
Route your agent's API calls through a local proxy that inspects requests and responses. This catches threats at the network level without modifying your application code.
Agent → Local Proxy (inspection) → LLM API
The proxy can block suspicious requests, log anomalies, and enforce rate limits.
2. Add input validation middleware.
Before prompts reach your model, pass them through validation that checks for: - Known injection patterns - Encoded or obfuscated content - Unusual character sequences - Attempts to override system instructions
3. Set up behavioral baselines.
Monitor your agent's normal behavior — typical API call frequency, files it accesses, external services it contacts. Alert when activity deviates significantly from baseline.
4. Configure cost circuit breakers.
Set spending thresholds that trigger alerts at 50%, 75%, and 90% of your budget. Consider automatic request throttling when limits approach.
Tools like AgentGuard360 bundle these capabilities — traffic inspection, content scanning, and cost monitoring — in a local proxy that watches your agent's activity without requiring code changes or cloud data routing.
What are common mistakes to avoid?
- Relying only on pre-deployment security scans
- Assuming prompt injection is someone else's problem
- No monitoring for cost anomalies until the invoice arrives
- Logging agent activity without actually reviewing it
- Treating runtime protection as optional for "internal" tools
