Lakera vs Trussed: Prompt Defense vs Runtime Governance
An architecture-first look at where prompt-level input defense ends and runtime policy enforcement for AI agents begins.
Two Different Risk Categories, Not Two Versions of the Same Control
Lakera and Trussed AI are often placed side by side in vendor shortlists, but they map to different OWASP risk categories and different moments in the agent lifecycle. Treating them as interchangeable options collapses two distinct control surfaces into one decision.
Lakera focuses on prompt-level defense: inspecting model inputs and outputs for adversarial or manipulative content before or as the model processes them. That posture aligns with OWASP Prompt Injection (LLM01).
Trussed AI focuses on runtime governance: enforcing agent identity, least-privilege permissions, and tool-call policy while the agent executes. That posture aligns with OWASP Excessive Agency, where the risk is not only what enters the model, but what the agent is allowed to do once it is running.
Two Control Surfaces, One Agent Lifecycle
Mapping each control against the stages of an agent’s execution clarifies why they do not substitute for one another.
Prompt-level defense
Inspects model inputs and outputs for adversarial or manipulative content before or as the model processes them. Primary fit: input and output risk, including prompt injection patterns.
Runtime governance
Enforces agent identity, least-privilege permissions, and tool-call policy while the agent executes. Primary fit: agency, permission scope, and tool misuse during live operation.
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Input and prompt handling
Prompt-level controls evaluate content directed at the model. This is where injection and adversarial input screening apply.
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Model processing and response
Output inspection can flag unsafe or manipulated model responses. It does not redefine what tools the agent may invoke next.
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Tool calls and agent actions
Runtime governance evaluates identity, permissions, and policy on each action. This is where excessive agency is constrained.
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Audit and operational evidence
Prompt defenses tend to log flagged prompts and responses. Runtime controls can produce auditable records of tool calls and permission decisions.
Control Surface Comparison
The table below summarizes how the two approaches differ across capability, lifecycle stage, and the risk each is designed to reduce.
| Dimension | Trussed AI | Lakera | Why it differs |
|---|---|---|---|
| Primary control surface | Runtime policy enforcement | Prompt-level input and output defense | Different stages of the agent execution lifecycle |
| OWASP alignment | Excessive Agency | Prompt Injection (LLM01) | Separate risk categories, not alternate products for the same risk |
| What is inspected or enforced | Agent identity, permissions, tool-call policy | Model inputs and outputs for adversarial content | Content filtering does not equal action authorization |
| Lifecycle emphasis | Live operational execution | Prompt handling and model I/O | Coverage gaps appear when only one stage is controlled |
| Typical evidence produced | Auditable tool calls and permission decisions | Logs of flagged prompts and related signals | Procurement and audit needs often require both forms of evidence |
What Remains Unaddressed If You Deploy Only One
Deploying only prompt-level defense leaves execution-time agency open. An agent can still misuse a tool it already has permission to access, because input filtering does not reduce the scope of permissions already granted. OWASP separates Prompt Injection from Excessive Agency for that reason: preventing tool misuse requires controls at the execution layer, not only at the input layer.
Deploying only runtime governance without prompt-level defense leaves adversarial input and output paths less inspected. Runtime policy can constrain actions, but it is not a substitute for inspecting model inputs and outputs for manipulative content.
MCP defines protocol primitives for capability negotiation and tool invocation at the host and application layer, but the specification does not itself mandate a specific enforcement mechanism. Enforcing identity, permission, and least-privilege policy over those tool calls requires a separate governance layer.
In short, comprehensive AI agent security typically requires both control surfaces rather than a choice between them. Buyers should evaluate Lakera and Trussed AI as potentially complementary, based on the OWASP risk categories each addresses, rather than as mutually exclusive options.
Questions to Ask Before Procurement
Use these questions to separate prompt defense from runtime governance when comparing vendors and architectures.
- Does this product inspect model inputs and outputs, or enforce policy on tool and agent actions, per OWASP’s risk categorization?
- Which AI agent lifecycle stages, as defined in NIST’s AI RMF, does this control actively cover: pre-deployment testing or live operational execution?
- If the agent architecture uses MCP or a similar tool-calling protocol, at which layer does the vendor’s control operate?
- Does the product produce auditable logs of individual tool calls and permission decisions, or only logs of flagged prompts?
- Has the vendor published independently verifiable technical documentation describing its detection or enforcement mechanisms?
Frequently asked questions
Are Lakera and Trussed AI direct competitors?
Based on the OWASP risk categories each addresses, they operate at different lifecycle stages rather than the same control surface. Lakera addresses prompt-level input risk; Trussed AI addresses execution-level agency and permission risk. Buyers should evaluate them as potentially complementary rather than mutually exclusive.
Can prompt-level defense prevent an agent from misusing a tool it already has permission to access?
No. OWASP separates Prompt Injection from Excessive Agency because input filtering does not reduce the scope of permissions already granted to an agent. Preventing tool misuse requires controls at the execution layer, not the input layer.
Does the Model Context Protocol itself enforce security policy?
MCP defines protocol primitives for capability negotiation and tool invocation at the host and application layer, but the specification does not itself mandate a specific enforcement mechanism. Enforcing identity, permission, and least-privilege policy over those tool calls requires a separate governance layer.
Evaluate Coverage Across the Full Agent Lifecycle
Understand where prompt-level defense ends and runtime policy enforcement begins before finalizing your AI agent security stack.
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