The Complete Guide to AI Agent
Security in Production

Why agent security is different

Traditional API security assumes a human in the loop. A user submits a request, your server validates it, returns a response. The attack surface is the request itself. AI agents break every one of those assumptions.

Agents make autonomous decisions. They choose which tools to call, what arguments to pass, and how to chain actions together. There is no human reviewing each step before it executes. A single prompt injection in a scraped web page can redirect an entire workflow.

Agents have tool access. They can read files, execute SQL, send HTTP requests, and call third-party APIs. This is not a hypothetical risk — it is the entire point of an agent. The same capabilities that make agents useful make them dangerous when compromised.

Agents process untrusted input as part of their core loop. User messages, tool outputs, retrieved documents, API responses — all of it flows into the LLM context window with no clear boundary between instructions and data. An attacker who controls any of those inputs can influence the agent's behavior.

The threat landscape

Agent threats fall into distinct categories, each exploiting different aspects of how agents operate. Here are the six you need to know about.

No single detection method catches everything. Regex misses rephrased attacks. Semantic analysis is too slow for every request. LLM judges are expensive at scale. The solution is layered scanning — each layer catches what the others miss, and only the events that need deeper analysis get passed up the chain.

Event arrives
  │
  ├─ L1 Pattern Scan (< 1ms)
  │    ├─ Match found → flag immediately
  │    └─ No match → pass to L2
  │
  ├─ L2 Semantic Scan (~5-15ms)
  │    ├─ High similarity to known attack → flag
  │    └─ Low similarity → pass to L3 (sampled)
  │
  └─ L3 Behavioral Analysis (~50-200ms)
       ├─ LLM judge: does this event look malicious in context?
       └─ Baseline comparison: is this normal for this agent?

Detection tells you something bad is happening. Policy enforcement prevents it from happening in the first place. Policies define what an agent is allowed to do — which tools it can call, what data it can access, which domains it can reach — and enforce those rules at runtime.

The most common security failure we see is not sophisticated attacks. It is agents with overly permissive tool access doing exactly what they were told, just with sensitive data in context. A customer service bot with execute_sql access. A content writer with send_email permissions. Policy enforcement eliminates this class of problem entirely.

version: "1.0"
rules:
  - name: block-prompt-injection
    scanner: prompt_injection
    action: block
    severity: critical

  - name: restrict-tools
    scanner: tool_access
    action: block
    config:
      allowed_tools:
        - search_knowledge_base
        - get_order_status
        - create_support_ticket
      # All other tools are denied by default

  - name: block-sensitive-data
    scanner: pii
    action: block
    severity: high
    config:
      blocked_entities:
        - credit_card
        - ssn
        - api_key

  - name: domain-deny-list
    scanner: exfiltration
    action: block
    config:
      blocked_domains:
        - "*.pastebin.com"
        - "webhook.site"
        - "*.ngrok.io"

You cannot fix what you cannot see. Every team we talk to says some version of the same thing: "We don't really know what our agents are doing in production." Agents produce orders of magnitude more events than traditional applications — dozens of tool calls per session, each with inputs and outputs that could contain sensitive data or malicious payloads.

Observability is not a nice-to-have for agent security. It is the foundation. Without it, attacks go undetected, data leaks go unnoticed, and policy violations accumulate silently.

Security that is hard to implement does not get implemented. Rune is designed to add runtime scanning to any agent in three lines of code, with zero changes to your agent logic.

from runesec import Shield

shield = Shield(api_key="rune_sk_...")

# Scan every input and output in your agent loop
result = shield.scan(
    agent_id="support-bot",
    input=user_message,
    output=agent_response,
    tool_calls=tool_calls,  # optional: list of tool calls
)

if result.blocked:
    # Rune blocked this event based on your policy
    return "I can't process that request."

# Otherwise, continue normally
return agent_response

The SDK works with any Python agent framework — LangChain, CrewAI, AutoGen, custom builds. It ships events to Rune's scanning engine asynchronously, so it adds negligible latency to your agent loop. Blocking decisions happen inline when configured.

from runesec.integrations import LangChainCallback

# Drop-in callback — no changes to your chain
chain = your_langchain_chain()
result = chain.invoke(
    {"input": user_message},
    config={"callbacks": [LangChainCallback(shield)]}
)

For a full walkthrough including policy configuration, dashboard setup, and alerting rules, see the getting started guide.