Processing boundary
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86
AI tools
Prompt Injection Risk Pre-Scan
Scans user, web, or RAG text with time-bounded explainable rules and reports role changes, ignore-previous-instruction phrases, secret or system-prompt requests, and encoded-command signals by line. It is not semantic classification, sandboxing, or a jailbreak guarantee.
✓ Free✓ No account✓ In-browser
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What does this tool do?
Find instruction override, secret requests, and tool-abuse signals with local rules. Prompt Injection Risk Pre-Scan limitation: The tool calls no remote model and neither generates nor verifies model output.
- Input
- For Prompt Injection Risk Pre-Scan, provide an instruction with an explicit goal, audience, context, constraints, and expected output format. The requested outcome is to find instruction override, secret requests, and tool-abuse signals with local rules.
- Output
- When Prompt Injection Risk Pre-Scan finishes, it returns an editable prompt draft, coverage metrics, and explicit improvement actions, organised around the goal to find instruction override, secret requests, and tool-abuse signals with local rules.
- Method
- Prompt Injection Risk Pre-Scan uses this disclosed method to find instruction override, secret requests, and tool-abuse signals with local rules: a rule-based review separates instruction components and calls no remote model.
- Verification
- Before accepting a Prompt Injection Risk Pre-Scan result, complete model testing with representative normal, missing-context, conflicting, sensitive-data, and prompt-injection cases; the evidence should support the goal to find instruction override, secret requests, and tool-abuse signals with local rules.
TOOL-SPECIFIC RUN PLAN
Go to the workbench ↓See exactly what Prompt Injection Risk Pre-Scan expects and returns
Prompt Injection Risk Pre-Scan uses the contract below to complete “First-pass RAG-content triage: local analysis with Prompt Injection Risk Pre-Scan” in particular. Confirm the shape with the example first; use real data only when the fields and expected result are clear.
- Use this shape
1 · Prepare the input
Prompt Injection Risk Pre-Scan — For Prompt Injection Risk Pre-Scan, provide an instruction with an explicit goal, audience, context, constraints, and expected output format. The requested outcome is to find instruction override, secret requests, and tool-abuse signals with local rules.. Paste only text you are authorized to inspect. Expected format for Prompt Injection Risk Pre-Scan: For Prompt Injection Risk Pre-Scan, provide an instruction with an explicit goal, audience, context, constraints, and expected output format. The requested outcome is to find instruction override, secret requests, and tool-abuse signals with local rules..
- Method applied
2 · Run the operation
Prompt Injection Risk Pre-Scan — Prompt Injection Risk Pre-Scan uses this disclosed method to find instruction override, secret requests, and tool-abuse signals with local rules: a rule-based review separates instruction components and calls no remote model. Run the local pre-scan and review each finding's line and rule. Prompt Injection Risk Pre-Scan applies this method: Prompt Injection Risk Pre-Scan uses this disclosed method to find instruction override, secret requests, and tool-abuse signals with local rules: a rule-based review separates instruction components and calls no remote model.
- Expected output
3 · Read the result
Prompt Injection Risk Pre-Scan — When Prompt Injection Risk Pre-Scan finishes, it returns an editable prompt draft, coverage metrics, and explicit improvement actions, organised around the goal to find instruction override, secret requests, and tool-abuse signals with local rules.. Checking tool-using agent input: validating the Prompt Injection Risk Pre-Scan output
- Acceptance check
4 · Accept or correct
Prompt Injection Risk Pre-Scan — Before accepting a Prompt Injection Risk Pre-Scan result, complete model testing with representative normal, missing-context, conflicting, sensitive-data, and prompt-injection cases; the evidence should support the goal to find instruction override, secret requests, and tool-abuse signals with local rules.. Verify with source trust, separate instruction/data channels, allowlists, and real model tests. Acceptance check for Prompt Injection Risk Pre-Scan: Before accepting a Prompt Injection Risk Pre-Scan result, complete model testing with representative normal, missing-context, conflicting, sensitive-data, and prompt-injection cases; the evidence should support the goal to find instruction override, secret requests, and tool-abuse signals with local rules..
A tool-specific example path
1. First-pass RAG-content triage: local analysis with Prompt Injection Risk Pre-Scan → 2. Checking tool-using agent input: validating the Prompt Injection Risk Pre-Scan output → 3. Preparing red-team scenarios: checking the limits of Prompt Injection Risk Pre-Scan
Tip: when an example-data button is available, run it first. Do not use the result in a live process unless it passes the acceptance check.
Persistent storage
Input and output are not stored. The optional usage counter keeps only tool identity and count, never content.
Verification
Output comes from disclosed rules or browser APIs and needs independent review before high-impact use.
APPLICATION AND DECISION GUIDE
✓ REVIEWEDUse Prompt Injection Risk Pre-Scan with the right input, acceptance check, and next step
Scans user, web, or RAG text with time-bounded explainable rules and reports role changes, ignore-previous-instruction phrases, secret or system-prompt requests, and encoded-command signals by line. It is not semantic classification, sandboxing, or a jailbreak guarantee. The notes below help you do more than produce a result: they show how to test whether Prompt Injection Risk Pre-Scan fits the task and when to stop before a weak output travels further.
Prompt Injection Risk Pre-Scan uses this disclosed method to find instruction override, secret requests, and tool-abuse signals with local rules: a rule-based review separates instruction components and calls no remote model. The tool uses no remote model or generative LLM. Local explainable heuristics produce suggestions while the user supplies context and the final decision.
For Prompt Injection Risk Pre-Scan, provide an instruction with an explicit goal, audience, context, constraints, and expected output format. The requested outcome is to find instruction override, secret requests, and tool-abuse signals with local rules. Confirm the shape first with a small example containing no personal data.
When Prompt Injection Risk Pre-Scan finishes, it returns an editable prompt draft, coverage metrics, and explicit improvement actions, organised around the goal to find instruction override, secret requests, and tool-abuse signals with local rules. — Before accepting a Prompt Injection Risk Pre-Scan result, complete model testing with representative normal, missing-context, conflicting, sensitive-data, and prompt-injection cases; the evidence should support the goal to find instruction override, secret requests, and tool-abuse signals with local rules.
Three practical use cases
First-pass RAG-content triage: local analysis with Prompt Injection Risk Pre-Scan
Action: Start with a small synthetic fixture that represents this need. Expected input: For Prompt Injection Risk Pre-Scan, provide an instruction with an explicit goal, audience, context, constraints, and expected output format. The requested outcome is to find instruction override, secret requests, and tool-abuse signals with local rules..
Acceptance signal: The fixture should reproduce “First-pass RAG-content triage: local analysis with Prompt Injection Risk Pre-Scan” without real personal data.
Checking tool-using agent input: validating the Prompt Injection Risk Pre-Scan output
Action: Keep that fixture unchanged and run the on-device method: Prompt Injection Risk Pre-Scan uses this disclosed method to find instruction override, secret requests, and tool-abuse signals with local rules: a rule-based review separates instruction components and calls no remote model.
Acceptance signal: Identical input should return the same result, with no network or file action assumed beyond the disclosed method.
Preparing red-team scenarios: checking the limits of Prompt Injection Risk Pre-Scan
Action: Retain the output record before moving it into the target workflow: When Prompt Injection Risk Pre-Scan finishes, it returns an editable prompt draft, coverage metrics, and explicit improvement actions, organised around the goal to find instruction override, secret requests, and tool-abuse signals with local rules..
Acceptance signal: Acceptance requires Before accepting a Prompt Injection Risk Pre-Scan result, complete model testing with representative normal, missing-context, conflicting, sensitive-data, and prompt-injection cases; the evidence should support the goal to find instruction override, secret requests, and tool-abuse signals with local rules.; otherwise do not move the result forward.
Do not use the result for a decision beyond this boundary: Prompt Injection Risk Pre-Scan limitation: The tool calls no remote model and neither generates nor verifies model output.
Move the result to another tool or live process only after Before accepting a Prompt Injection Risk Pre-Scan result, complete model testing with representative normal, missing-context, conflicting, sensitive-data, and prompt-injection cases; the evidence should support the goal to find instruction override, secret requests, and tool-abuse signals with local rules.. Keep this limit visible in the decision record: Prompt Injection Risk Pre-Scan limitation: The tool calls no remote model and neither generates nor verifies model output.
A result in three steps
- 01
Paste only text you are authorized to inspect. Expected format for Prompt Injection Risk Pre-Scan: For Prompt Injection Risk Pre-Scan, provide an instruction with an explicit goal, audience, context, constraints, and expected output format. The requested outcome is to find instruction override, secret requests, and tool-abuse signals with local rules..
- 02
Run the local pre-scan and review each finding's line and rule. Prompt Injection Risk Pre-Scan applies this method: Prompt Injection Risk Pre-Scan uses this disclosed method to find instruction override, secret requests, and tool-abuse signals with local rules: a rule-based review separates instruction components and calls no remote model.
- 03
Verify with source trust, separate instruction/data channels, allowlists, and real model tests. Acceptance check for Prompt Injection Risk Pre-Scan: Before accepting a Prompt Injection Risk Pre-Scan result, complete model testing with representative normal, missing-context, conflicting, sensitive-data, and prompt-injection cases; the evidence should support the goal to find instruction override, secret requests, and tool-abuse signals with local rules..
When is this tool useful?
- ✓ First-pass RAG-content triage: local analysis with Prompt Injection Risk Pre-Scan
- ✓ Checking tool-using agent input: validating the Prompt Injection Risk Pre-Scan output
- ✓ Preparing red-team scenarios: checking the limits of Prompt Injection Risk Pre-Scan
Tool-specific limitation
Prompt Injection Risk Pre-Scan limitation: The tool calls no remote model and neither generates nor verifies model output.
LEARN THE WORKFLOW
Guides for this tool
Browser-Only Agentic AI: Designing Safe Tool Orchestration
Combine semantic search, multi-step plans, visible rationale, error translation, and on-device voice without uploading data.
Read guide →Local Security Guide to Web Crypto, RAG, and Prompt Injection
Apply passphrases, HMAC, SRI, CIDR, RAG budgets, and injection pre-scans with correct security boundaries.
Read guide →ABOUT THIS TOOL
Frequently asked questions
What input does Prompt Injection Risk Pre-Scan accept?+
For Prompt Injection Risk Pre-Scan, provide an instruction with an explicit goal, audience, context, constraints, and expected output format. The requested outcome is to find instruction override, secret requests, and tool-abuse signals with local rules. Paste only text you are authorized to inspect. Expected format for Prompt Injection Risk Pre-Scan: For Prompt Injection Risk Pre-Scan, provide an instruction with an explicit goal, audience, context, constraints, and expected output format. The requested outcome is to find instruction override, secret requests, and tool-abuse signals with local rules..
What does Prompt Injection Risk Pre-Scan return?+
When Prompt Injection Risk Pre-Scan finishes, it returns an editable prompt draft, coverage metrics, and explicit improvement actions, organised around the goal to find instruction override, secret requests, and tool-abuse signals with local rules. Prompt Injection Risk Pre-Scan uses this disclosed method to find instruction override, secret requests, and tool-abuse signals with local rules: a rule-based review separates instruction components and calls no remote model.
How should I validate Prompt Injection Risk Pre-Scan output?+
For “First-pass RAG-content triage: local analysis with Prompt Injection Risk Pre-Scan”, first complete “Run the local pre-scan and review each finding's line and rule. Prompt Injection Risk Pre-Scan applies this method: Prompt Injection Risk Pre-Scan uses this disclosed method to find instruction override, secret requests, and tool-abuse signals with local rules: a rule-based review separates instruction components and calls no remote model.”, then apply this check: “Verify with source trust, separate instruction/data channels, allowlists, and real model tests. Acceptance check for Prompt Injection Risk Pre-Scan: Before accepting a Prompt Injection Risk Pre-Scan result, complete model testing with representative normal, missing-context, conflicting, sensitive-data, and prompt-injection cases; the evidence should support the goal to find instruction override, secret requests, and tool-abuse signals with local rules..”. Do not use a consequential result before a second test with boundary or malformed input.
Does this tool send or store input on a server?+
No. Processing runs in this browser tab and tool input is not persisted. Copying, downloading, or transferring happens only when you choose it.