01
The system prompt carries the whole policy
Sensitive rules and permissions live in instructions the model can misunderstand, expose, or ignore.
AI tooling · Prompt injection controls
Limit what a manipulated prompt can reach and do.
Prompt injection can't be solved with one instruction or filter. The stronger approach limits the model's authority, separates trusted instructions from outside content, enforces policy in code, and requires approval before high-impact actions.
Visible signals
Prompt risk becomes business risk when the model has reach.
Manipulated content matters most when it can cross a trust boundary, reveal protected context, misuse a tool, or trigger action without informed approval.
02
Outside content looks like trusted instruction
Documents, websites, messages, and tool results enter the same context as authorized directions.
03
The model can reach too much
Broad data access, shared credentials, or powerful tools extend far beyond the task.
04
Approval happens after the action
People review a summary after the workflow has already sent, changed, disclosed, or deleted something.
05
Tests look for phrases, not outcomes
A filter catches known jailbreak language but misses hidden instructions, tool misuse, or data exposure.
06
An incident can't be rebuilt
The organization lacks the source, model context, policy decision, tool calls, approval, and outcome.
Control layers
Put enforceable boundaries outside the model.
Model instructions can help, but they shouldn't carry the full security burden. Layer controls so one bad judgment can't create an unauthorized result.
Which sources and instructions are trusted?
Classify sources and preserve provenance so outside content doesn't inherit system authority.
What is the model allowed to decide?
Define permitted inputs, expected outputs, prohibited behavior, refusal, and escalation.
What is the least access this task needs?
Limit credentials, records, tools, actions, destinations, and access duration.
Which rules must code enforce?
Use application logic for authorization, validation, allowlists, data rules, and transaction limits.
Which actions need informed human approval?
Require approval before high-impact actions and show the source, action, system, and uncertainty.
How will controls improve as the system changes?
Test direct, indirect, encoded, multimodal, and tool-based attacks against real consequences.
Control path
Trace authority from outside content to real consequence.
Follow a real interaction from input through action. At each step, decide what the system trusts, what the model can do, and which rule runs outside it.
01
Ingest
Where did the content come from?
Source, trust level, provenance, format, transformation, retrieval path, and handling rules.
02
Interpret
Which instructions should affect the task?
Application intent, user authority, task boundary, outside content, conflicts, and escalation.
03
Decide
Is the proposed output or action allowed?
Policy, authorization, schema, sensitive data, destination, limits, and validation.
04
Act
What consequence can the system create?
Tool, permission, credential, transaction limit, human approval, reversibility, and effect.
05
Review
Can the team detect and learn from misuse?
Trace, blocked attempt, approval, outcome, incident response, test result, and updated control.
Evidence
Review the whole application, not only the prompt.
The evidence includes every source of instruction, every system the model can reach, the code enforcing policy, the people approving action, and the final outcome.
01
Instruction assembly
System directions, user input, templates, memory, retrieved context, tool results, and their order.
02
Outside content
Web pages, files, messages, knowledge sources, images, metadata, and other untrusted material.
03
Tools and permissions
Functions, records, API scopes, destinations, limits, secrets, boundaries, and authorization code.
04
Outputs and downstream use
Responses, structured data, links, code, routing, system updates, and the people or services that rely on them.
05
Approval and recovery
Human checkpoints, displayed context, cancellation, rollback, escalation, and incident ownership.
06
Tests and outcomes
Attack simulations, blocked attempts, false alarms, tool calls, approvals, incidents, and customer effects.
No foolproof prompt layer
The goal is to reduce exposure, enforce authority, detect unsafe behavior, and limit impact. One prompt or filter can't remove prompt injection risk.
Common failure patterns
Controls fail when language is mistaken for authority.
The weakest designs ask the model to enforce rules that belong in identity, policy, application logic, permissions, approval, and incident response.
01
A classifier becomes the firewall
Input filtering stands alone even though unsafe influence can arrive indirectly or through ordinary-looking output.
02
Outside content is labeled but still followed
The application marks content as untrusted but gives the model the same context and permissions to act on it.
03
The model judges its own authority
Natural-language rules replace identity checks, scoped permissions, and policy enforced in code.
04
Human review becomes a confirmation click
Approvers see a summary without the source, affected records, uncertainty, or reason for review.
05
Testing stops at direct jailbreaks
Tests miss compromised documents, retrieved content, tool output, chained actions, and data theft.
06
Controls stay fixed while the workflow grows
New models, tools, sources, and permissions expand risk without a new review.
Practical outputs
Turn prompt safety into an operating control model.
The result shows how the workflow limits authority, validates important behavior, involves people, records evidence, and changes as threats evolve.
01
Trust boundary map
Instructions, outside content, model context, tools, data, people, systems, and unclear authority.
02
Layered control design
Task limits, source separation, least privilege, policy, validation, approval, and recovery.
03
Adversarial test plan
Scenarios tied to real consequences, including indirect injection, tool misuse, data exposure, and unsafe action chains.
04
Monitoring and response model
Trace requirements, thresholds, owners, containment, rollback, evidence, and control updates.
Engagement fit
Use these controls when AI can affect more than its own answer.
This work fits a defined AI use case that processes outside content and can reach sensitive data, tools, or important actions. It supports, but doesn't replace, application security, penetration testing, privacy, legal review, or incident response.
Start a fit checkRelated diagnostic paths
Start with the workflow and consequence the controls must protect.
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The interaction crosses customer workflows, service rules, systems, ownership, and handoffs.
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The model reads or changes CRM data, recommends customer action, or triggers automation.
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