LLM Security Beyond Prompt and Response Filtering
LLM applications become higher risk when model output drives tools, workflows, and API calls. Rutile reduces the operational blast radius by governing identity, permissions, runtime policy, and evidence around LLM-driven actions.
What is LLM security?
LLM security protects applications built on large language models from prompt injection, sensitive data disclosure, insecure output handling, excessive agency, retrieval weaknesses, model abuse, and unsafe downstream actions.
Search intent this page answers
LLM security searchers often want practical controls, not only taxonomy.
- What is LLM security?
- How do you prevent prompt injection from triggering tool calls?
- How does OWASP LLM Top 10 map to controls?
- How should LLM outputs be audited?
Risk areas
LLM security becomes business-critical when model responses can trigger real actions.
| Risk | Why it matters | Rutile response |
|---|---|---|
| Prompt injection | Instructions hidden in user or third-party content manipulate model behavior. | Policy checks and tool-call approval before execution. |
| Sensitive information disclosure | Models or tools expose secrets, PII, system prompts, or internal documents. | Data-scoped access and audit trail. |
| Excessive agency | LLM apps can take high-impact actions with insufficient constraints. | Temporary permission and runtime kill switch. |
| Insecure output handling | Generated output is trusted by downstream systems without validation. | Execution mediation and decision logging. |
Rutile control model
Rutile complements LLM gateways and firewalls by controlling what the LLM-driven workflow is allowed to do.
| Control | Implementation pattern | Rutile capability |
|---|---|---|
| Model-to-tool boundary | Separate model reasoning from tool authority. | Tool Proxy. |
| Permission boundaries | Issue access only for the approved action and resource. | JIT/JEA Broker. |
| Outcome evidence | Record prompt hash, tool, resource, policy, and result. | Audit Reporting. |
| Revocation | Stop risky sessions during runtime. | Runtime Monitoring & Kill Switch. |
Primary references
These references define LLM-specific security risk language.
OWASP Top 10 for Large Language Model Applications
Defines critical LLM application risks including prompt injection, sensitive information disclosure, excessive agency, and vector or embedding weaknesses.
OWASP Prompt Injection
Explains prompt injection as a vulnerability class affecting LLMs, chatbots, and autonomous agents.
NIST AI RMF Generative AI Profile
Applies NIST AI RMF concepts to generative AI risks and mitigation practices.
Related AI security topics
LLM Security FAQ
Does Rutile replace an LLM firewall?+
No. Rutile complements prompt and response controls by governing identity, tool execution, permissions, and evidence.
Why does LLM security need runtime controls?+
Because the highest business impact often occurs after a model decides to call a tool, access data, or trigger a workflow.
Rutile control model
Rutile complements LLM gateways and firewalls by controlling what the LLM-driven workflow is allowed to do.