LLM Security - Secure LLM Apps and Agents in Production
LLM security is not a model setting. It is a runtime control plane that inspects inputs, constrains actions, and records what happened, in front of every LLM app you ship.
Direct answer
LLM security is the practice of protecting applications and agents built on large language models from attacks like prompt injection and data exfiltration, and from the risks of giving a non-deterministic model real capabilities. AgentShield delivers LLM security at runtime: it blocks prompt injection, applies data-loss prevention, enforces least-privilege tool and data permissions, and writes an immutable audit trail. It maps to the OWASP Top 10 for LLM Applications, the canonical risk taxonomy for this space.
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INSPECTINGPolicy trace
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Audit trail
- § · → → →
The risk
LLM apps read untrusted text and then act, so a single poisoned input can hijack the app, leak data, or trigger a destructive action with no record of how it happened.
How AgentShield handles it
AgentShield wraps your LLM app as a control plane. It treats every external input as untrusted and inspects it, classifies and gates sensitive data on egress, restricts which tools and resources the app may use, and logs every action immutably for review and compliance. You keep your model and framework and add the security layer in front.
The controls
The controls that secure your LLM applications.
What are the biggest LLM security risks?
The biggest LLM security risks are not the model saying something rude. They are the application around the model reading untrusted text and then acting on it, which turns a language problem into a security one. The OWASP Top 10 for LLM Applications is the canonical list, and in production it collapses to a handful of things that actually cause incidents.
| Risk | What goes wrong | The control that contains it |
|---|---|---|
| Prompt injection | Hidden instructions hijack the app's behavior | Inspect every input, gate the resulting action |
| Sensitive data disclosure | The app leaks secrets or personal data in output | Data-loss prevention on egress |
| Excessive agency | The app can take actions far beyond its job | Least-privilege tool and data permissions |
| Supply chain and tool poisoning | A compromised tool or plugin feeds bad instructions | Inspect tool output, scope tool access |
| Missing accountability | No record of what the app did or why | Immutable, attributed audit trail |
We break the full list down in the OWASP LLM Top 10 explained. The pattern across all of them is the same: the model is non-deterministic and reads untrusted input, so you cannot make the app safe by tuning the prompt. You make it safe by constraining what it can do.
LLM security vs model safety
These get conflated, and the confusion is expensive. Model safety is about the model's own behavior: does it refuse harmful requests, avoid toxic output, stay aligned. LLM security is about the system: can an attacker make your application leak data, call the wrong tool, or take an action it should not. A model can be perfectly safe and your app still wide open.
| Model safety | LLM security | |
|---|---|---|
| Question it answers | Does the model behave well | Can the system be attacked |
| Owned by | Mostly the model provider | You, the application builder |
| Fixed by | Training, alignment, refusals | Runtime controls around the model |
| Example failure | Model writes something offensive | Injection makes the app email your data out |
The takeaway is that buying a safer model does not secure your LLM app. The security lives in the layer you add around it, which is why it belongs in a control plane you own rather than in the provider's weights.
FAQ
Common questions about llm security.
What is LLM security?
LLM security is the practice of protecting applications and agents built on large language models from attacks like prompt injection and data exfiltration, and from the risk of giving a non-deterministic model real capabilities. It works at runtime, around the model: inspecting untrusted input, enforcing what the app may do, preventing data leakage, and recording every action. It is distinct from model safety, which concerns the model behaving well on its own.
How do you secure an LLM application?
You secure an LLM application by treating every external input as untrusted and inspecting it for injection, scoping the app to least-privilege tool and data access, gating sensitive data on egress, holding high-risk actions for human approval, and logging every action to an immutable audit trail. The controls live in a layer in front of the model, so they hold even when the model is manipulated. Tuning the prompt alone does not secure the app.
What is the OWASP Top 10 for LLMs?
The OWASP Top 10 for LLM Applications is the industry-standard list of the most critical security risks in LLM-based systems, including prompt injection, sensitive information disclosure, supply chain vulnerabilities, and excessive agency. It is the canonical risk taxonomy for this space, and most practical LLM security work maps directly to closing the items on it with runtime controls.
Is LLM security different for agents?
Yes. A plain LLM app generates text, so its main risks are injection and data disclosure. An agent also takes actions with tools, which adds excessive agency and unsafe tool calls to the threat model. Securing an agent means everything you do for an LLM app plus enforcing tool and data permissions and gating irreversible actions, because now the model can do things, not just say them.
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