Harden an AI System Against Injection and Misuse
Architectural controls that survive a manipulated model.
/ quick answer
Assume the model can be turned against you and remove its ability to do damage. Architectural controls that survive a manipulated model.
- 01Map every tool the model can reach and classify read / write / irreversible.
- 02Remove generic escape hatches: raw SQL, shell, arbitrary HTTP.
- 03Enforce authorisation server-side, per user, on every call — never in the prompt.
- 04Treat all retrieved content as hostile input; never let it change the agent's goal.
- 05Filter output for secrets, tokens and PII before it leaves the system.
- 06Require approval for money movement, deletions and external communication.
- 07Log every call with user, args and result; alert on anomalous patterns.
- 08Record the system in the AI register with an owner and a review date.
/ frequently asked
What does the Harden an AI System Against Injection and Misuse workflow do?
Assume the model can be turned against you and remove its ability to do damage.
What problem does Harden an AI System Against Injection and Misuse solve?
System-prompt instructions like 'never reveal secrets' are not a security control.
How many steps does Harden an AI System Against Injection and Misuse take?
8 steps. It starts with map every tool the model can reach and classify read / write / irreversible. and ends with record the system in the ai register with an owner and a review date..
How do I set up Harden an AI System Against Injection and Misuse step by step?
1) Map every tool the model can reach and classify read / write / irreversible. 2) Remove generic escape hatches: raw SQL, shell, arbitrary HTTP. 3) Enforce authorisation server-side, per user, on every call — never in the prompt. 4) Treat all retrieved content as hostile input; never let it change the agent's goal. 5) Filter output for secrets, tokens and PII before it leaves the system. 6) Require approval for money movement, deletions and external communication. 7) Log every call with user, args and result; alert on anomalous patterns. 8) Record the system in the AI register with an owner and a review date.
Which tools does Harden an AI System Against Injection and Misuse need?
It uses ai-security-stack — each linked below with its own node.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →AI Security
AI security protects systems where the model is an untrusted decision-maker acting on untrusted input with real tool access.
- →Prompt Injection
An attack where hostile input hijacks the LLM's instructions, causing it to leak data or misbehave.
- →AI Governance
AI governance is the set of policies, records and reviews that make an organisation's AI use accountable and auditable.
- →AI Red Teaming
Deliberately attacking an AI system — jailbreaks, prompt injection, data leaks — to find failures before real users do.
Related workflows
Turn this into a repeatable process.
- →Audit MCP Tool Security
A checklist that catches the failure modes unique to model-driven tool calls.
- →Build an Eval Suite Before Optimising Prompts
Stop guessing whether a change improved anything.
- →Red Team an AI App
A repeatable process for attacking your own AI product before launch.
- →Secure Terminal Agent Execution
This workflow details how to safely execute AI-generated commands and scripts from a terminal coding agent, mitigating risks associated with arbitrary code execution and sensitive data access. It emphasizes a human-in-the-loop approach.
Related tool stacks
The tools that run it in production.
- →AI Security Stack
Least-privilege tooling, approval gates and audit trails for agentic systems.
- →AI Observability Stack
Traces, cost, evals and quality drift for AI systems in production.
Related prompts
Reusable prompts for this job.
- →AI System Threat Model Prompt
Produces a concrete threat model for an AI system with tool access.
- →Refactor and Security Review Prompt
Clean up a vibe-coded app and catch security gaps before launch.
- →Autonomy Boundary Prompt
Defines exactly what an agent may do alone, with approval, or never.
- →Eval Rubric Prompt
Builds a scoring rubric a grader model can apply consistently.
Related use cases
How people apply it, and what came out.
- →Platform Catches a 19% Quality Drop Before Users Did
Continuous sampling and evals caught silent degradation after a model update.
Comparisons & alternatives
Pick between the options.
- →Model-Graded Evals vs Assertion Evals
Assertions are cheap, fast and objective; model grading captures quality you cannot express as a rule.
- →Hot Wallet vs Cold Wallet
Hot wallets trade convenience for exposure; cold wallets trade friction for a signing key that never touches an internet-connected device.