AI SECURITY WORKBENCH · ADVERSARIAL TESTING

Replayable Adversarial Flow Testing

Exercise realistic AI failure flows and preserve what happened.

Run bounded adversarial scenarios across prompts, retrieval, agents, tools, identities, permissions, multimodal inputs, workflows, outputs, and policy controls. Record the environment, inputs, control state, observed behavior, consequence, evidence, and retest status so reproduced failures become durable regression assets.

Scope an AI Red Team Open the Adversarial Range

WHICH FLOWS FAIL UNDER ADVERSARIAL PRESSURE?

Scenario coverage

Organize prompt, retrieval, agent, tool, multimodal, workflow, authority, supply-chain, and model-integrity tests into versioned scenario packs.

Control mapping

Relate scenario objectives and observed failures to relevant control language without treating framework alignment as proof of coverage or compliance.

Evidence-preserving

Record scenario context, inputs, environment, control state, observed behavior, consequence, outcome, and reviewer notes.

Replay and regression

Turn supported failures into versioned fixtures that can be rerun after model, prompt, retrieval, permission, workflow, or control changes.

AI Security Workbench · Replayable Adversarial Flow Testing

Adversarial Range

157 scenarios

Scenario coverage

Prompt, retrieval, agent, tool, multimodal, workflow, authority, supply-chain, and model-integrity tests organized into 15 versioned scenario packs.

Control mapping

Relate scenario objectives and observed failures to relevant control language without treating framework alignment as proof of coverage.

Evidence-preserving

Record scenario context, inputs, environment, control state, observed behavior, consequence, outcome, and reviewer notes.

Replay and regression

Turn supported failures into versioned fixtures that can be rerun after model, prompt, retrieval, permission, workflow, or control changes.

Failure classes exercised

Prompt and instruction manipulation, retrieval and corpus poisoning, agent and tool misuse, authority and approval bypass, data exposure, unsafe output propagation, multimodal manipulation, supply-chain and model-integrity failure, and denial of service and resource abuse.

Prompt InjectionTool AbuseRAG PoisoningMultimodal AbuseSupply Chain

AI Security Workbench · Replayable Adversarial Flow Testing

Adversarial Range

157 scenarios

Scenario coverage

Prompt, retrieval, agent, tool, multimodal, workflow, authority, supply-chain, and model-integrity tests organized into 15 versioned scenario packs.

Control mapping

Relate scenario objectives and observed failures to relevant control language without treating framework alignment as proof of coverage.

Evidence-preserving

Record scenario context, inputs, environment, control state, observed behavior, consequence, outcome, and reviewer notes.

Replay and regression

Turn supported failures into versioned fixtures that can be rerun after model, prompt, retrieval, permission, workflow, or control changes.

Failure classes exercised

Prompt InjectionTool AbuseRAG PoisoningMultimodal AbuseSupply Chain

157

Scenario files in the current registry

Active attack packs

Tool adapters

Threat vectors represented

Core capabilities

What Adversarial Range does.

Prompt and instruction testing

Exercise direct and indirect injection, instruction conflict, role confusion, policy bypass, prompt disclosure, and cross-context manipulation.

Retrieval and context testing

Exercise poisoned content, authorization failure, tenant-boundary failure, provenance loss, context leakage, and unsafe retrieval-to-action propagation.

Agent and tool testing

Exercise excessive agency, unsafe tool use, approval bypass, argument manipulation, identity misuse, dangerous composition, and unauthorized external effects.

Multimodal and document testing

Exercise hidden instructions and unsafe transformations across images, documents, markup, metadata, and multimodal inputs.

Model and data integrity testing

Exercise supported scenarios for backdoors, poisoning, inversion, membership, drift, artifact integrity, and supply-chain failure.

Coverage and gap analysis

Distinguish explicit scenario coverage, inferred framework coverage, untested conditions, unsupported claims, and evidence gaps.

Replay and regression

Preserve supported failures as versioned test cases with environment, input, expected control, observed behavior, assertions, evidence hooks, and retest state.

Evidence & signals

What you get out of the box.

Failure Classes

Prompt Injection
Data Exfiltration
Tool Abuse
RAG Poisoning
Multimodal Abuse
Supply Chain Poisoning
Model Integrity
DoS

Outcome States

passed
blocked
detected
degraded
failed
partial
inconclusive
not run

Evidence and regression outputs

Evidence bundle
Results report
Control mapping
Structured scenario result
Trace references
Regression fixture

Red team + Blue team

Built for both sides of the security equation.

Red Team Use

Exercise realistic failure flows across prompt injection, retrieval, agent authority, multimodal input, tool use, approval boundaries, output handling, and data exposure.
Run supported adapters against the same versioned scenario definitions while preserving adapter, environment, model, and control differences.
Create regression fixtures from supported reproduced failures and preserve the evidence required for later comparison.

Blue Team Use

Convert supported failures into assigned remediation, control changes, release conditions, and retest requirements.
Use explicit coverage and evidence state instead of framework-name counts as a proxy for assurance.
Rerun scenarios after prompt, model, retrieval, tool, permission, workflow, or policy changes.

A useful scenario contains more than a prompt and an expected answer.

A useful scenario defines the system context, actor, objective, preconditions, input, ordered flow, expected control, safe execution boundary, evidence requirements, success condition, and outcome state.

RANGE-01

Scenario Pack Architecture

Reusable scenario packs organize prompt, retrieval, tool, authority, multimodal, and workflow abuse into repeatable tests.

Core

Controlled adversarial range

Shared contracts

Scenario execution
Evidence capture
Replay and regression

Prompt and instruction

Prompt injection • Policy conflict

Retrieval and context

Indirect prompt injection • Poisoned or hostile content

Tools and authority

Tool misuse • Unsafe delegated authority

Multimodal and workflow

Hostile multimodal input • Workflow manipulation

This makes scenarios replayable across versions, models, guardrails, retrieval changes, tool permissions, and deployment environments instead of producing isolated screenshots or anecdotal jailbreak results.

Execute inside a versioned, authorized boundary.

Scenario packs retain environment, model, control, and evidence metadata. Not run, blocked, detected, degraded, failed control, partial, and inconclusive states remain explicit, and CI or release-gate use is limited to supported execution modes.

Turn every reproduced failure into a regression asset.

A confirmed failure should become a versioned test case with preserved inputs, environment, expected control behavior, observed result, evidence, and retest state.

RANGE-02

Evaluation-to-Regression Lifecycle

An observed failure becomes durable security capability when it is converted into a replayable regression fixture.

Evaluation lifecycle

1
Define the scenario
Record target assumptions, preconditions, allowed actions, and expected evidence.
2
Execute under control
Run within explicit authorization, data, rate, and environment limits.
3
Observe the result
Capture model, retrieval, tool, agent, and external behavior.
4
Reproduce and challenge
Repeat the behavior and test alternative explanations.
5
Create the regression fixture
Preserve the minimum replayable inputs, assertions, and evidence hooks.
6
Rerun after change
Determine whether the failure is closed, residual, rejected, or still reproducible.

Retest loop returns to Execute under control

What did the rerun prove?

Closed
Residual
Still reproducible
Inconclusive

The value compounds when the same case can prove a fix, detect regression, compare models or controls, and support a defensible release decision.

Choose the operator model deliberately.

Analyst-guided exploration, model-assisted scenario variation, and deterministic replay answer different questions. Automation supports breadth and repeatability; analyst judgment remains necessary for hypothesis formation, consequence analysis, ambiguity, and claim approval.

Use automation for coverage and humans for judgment.

Automated execution is well suited to repeatable scenarios, permutations, regression testing, and evidence collection. Human-guided testing remains essential for hypothesis formation, adaptive abuse, ambiguous behavior, consequence analysis, and claim review.

RANGE-03

Human-Guided and Autonomous Modes

Analyst-guided, model-guided, and replay-driven testing serve different purposes and require different controls.

The strongest operating model combines both modes rather than presenting autonomy as a substitute for expert adversarial reasoning or analyst approval.

Delivery & licensing

Available through the model that fits the product outcome.

Expert-led engagement

AI Security LLC runs scenario packs directly against a scoped target as part of an assessment.

Bounded partner pilot

One attack-pack category is run against a representative fixture and returned as evidence.

OEM or licensed capability

The scenario harness and control-mapping engine can run behind a partner's own testing or CI surface.

Accepts

System context, target configuration, and a representative fixture or replay input.

Returns

Scenario outcome, observed trace references, control state, evidence bundle, replay fixture, and explicit lifecycle state.

Current maturity

Fixture-tested

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AI SECURITY WORKBENCH

Ready to exercise one representative failure flow?

Start with one workflow, retrieval boundary, agent capability, tool action, output sink, or control decision. Use Adversarial Range to run bounded scenarios, preserve observed outcomes, and turn supported failures into remediation and regression evidence.

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Threat Canvas

Use system and flow context to define bounded scenarios.

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Authority Graph

Use authority and approval context to design agent and tool tests.

Explore

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Attack Path Analysis

Determine whether reproduced failures support wider consequential paths.