AI Engineering · Version 1.7.0 · Reviewed 2026-08-02

LLM Red-Team Designer

Name: LLM Red-Team Designer
Brand: SkillVault
Price: 99 INR
Availability: InStock

Make AI behavior measurable and safer for adversarial test design and tool-abuse scenarios with evidence, explicit trade-offs, and a verification plan.

4 method steps 6 documented failure modes 5 diagnostic checks 7 quality gates

Creates threat-based adversarial evaluations for prompt injection, data leakage, harmful actions, tool abuse, jailbreaks, and policy bypass.

₹99 one-time

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What this skill helps you do

Adversarial test design
Tool-abuse scenarios
Safety regression suites

How LLM Red-Team Designer works

You provide

Prompts, model versions, evaluation data, and failures

It inspects

Failure class and context sufficiency for adversarial test design

It decides

A tool-abuse scenarios change with one variable moved

You verify

Pass rate per case class against a pinned baseline

What it checks first

LLM Red-Team Designer creates threat-based adversarial evaluations for prompt injection, data leakage, harmful actions, tool abuse, jailbreaks, and policy bypass. Use it when the work involves Adversarial test design, Tool-abuse scenarios, Safety regression suites.

Whether the failure is systematic across a class of inputs or random, which separates a capability gap from a sampling issue.
Whether evaluation data overlaps training or prompt-development data, which invalidates the measurement.
Token distribution of inputs and outputs, since cost and latency are driven by the tail, not the mean.
Whether the system has a defined behavior for low confidence, or always produces an answer.
Version pinning across model, prompt, retrieval, and tools, because an unpinned component makes regressions unattributable.

Failure modes it recognizes

Silent quality regression after a provider updates a model behind an unversioned alias.
Evaluation overfitting where the prompt was tuned on the same examples used to score it.
Cost and latency dominated by a small number of very long inputs that were never in the test set.
Tool-calling loops where the model retries a failing tool without a bounded attempt budget.
Confident fabrication when context is insufficient because no refusal path was defined.
Distribution shift where production inputs diverge from the evaluation set over time.

Answers it will reject

Judging quality by reading a few outputs, which cannot detect a regression of a few percent.
Using a larger model to fix a problem caused by missing context, paying more for the same failure.
Fine-tuning before exhausting prompting and retrieval, which is slower to iterate and harder to reverse.
Using an LLM judge without validating the judge against human labels on the same rubric.

Decision rules it applies

Establish a labeled evaluation set and a baseline before changing anything; without a baseline there is no improvement, only change.
Pin every version and change one component at a time.
Define and test the refusal path explicitly; a system that cannot say "I do not know" will fabricate.
Budget latency and cost on p95 token counts, not averages.

Evidence it asks for

Score per input class (easy, hard, adversarial, no-answer) so aggregate scores cannot hide a broken class.
Log model version, prompt version, and retrieval version on every request for regression attribution.
Track p50 and p95 tokens and cost per successful task, not per call.

The method inside

Turn adversarial test design into explicit functional requirements and quality-attribute constraints.
Model the critical path, state, trust, and failure boundaries that govern tool-abuse scenarios.
Compare viable designs for safety regression suites against weighted constraints and operational ownership.
Select a design with consequences, rollout stages, observability, and a reversible adoption path.

Deliverables

Adversarial test design assessment
Tool-abuse scenarios decision and action plan
Safety regression suites verification checklist

Evidence requirements

Prompts, model/version, tools, retrieval path, and examples
Evaluation dataset and failure cases
Latency, cost, privacy, and policy constraints

Quality gates

Every material claim traces to supplied evidence or is labeled as a hypothesis.
The response follows the declared deliverable contract.
No execution, access, measurement, or verification is invented.
Secrets and personal data are redacted rather than repeated.
The user receives a concrete independent verification step.
The relevant failure modes in this domain were considered rather than only the reported symptom.
No listed anti-pattern was recommended as a solution.

Example task

Input

Build a red-team plan for an internal agent that reads documents and can create support tickets.

Expected output

Prioritize indirect prompt injection in documents, cross-user data exfiltration, and ticket creation with attacker-controlled fields. Define success as unauthorized effect, not merely undesirable text, and test controls independently...

Boundaries and compatibility

Ideal for

Adversarial test design: produce a decision or artifact grounded in supplied evidence.
Tool-abuse scenarios: produce a decision or artifact grounded in supplied evidence.
Safety regression suites: produce a decision or artifact grounded in supplied evidence.

Out of scope

Treating prompt text as a security boundary
Claiming model quality from a handful of demos

Agent compatibility

GitHub Copilot custom agents
Claude Agent Skills / SKILL.md
Any instruction-following chat model

Tool policy: Advisory by default. No tools are assumed. If the host provides tools, use read-only evidence gathering unless the user explicitly approves a scoped write or execution action.