AI and machine-learning teams
Ship AI that has been attacked before your customers trust it.
Enterprise buyers increasingly ask whether AI systems have been red-teamed. Your ML team knows how to build models. That does not mean it should have to become an offensive AI-security team.
The distinction
Functional testing tells you whether the model works.
Security testing asks whether it can be manipulated.
An AI feature can pass every normal product test and still behave unexpectedly under hostile input. The relevant questions are different:
- Can a user override intended instructions?
- Can untrusted content alter model behavior?
- Can the model reveal hidden instructions?
- Can it disclose sensitive context?
- Can a jailbreak bypass an application control?
- Did a fine-tuned model introduce a new security regression?
- Is an inference endpoint exposed or weakly configured?
Direct prompt injection
A user supplies instructions designed to override or bypass the expected control model. RedMaw tests whether the AI feature can be pushed to ignore intended instructions, reveal restricted information, disclose hidden behavior, bypass application-level controls or enter an unintended security state.
The important result is not that the model says something strange. It is whether the behavior creates a meaningful security outcome.
Indirect prompt injection
The malicious instruction does not have to come from the user. It can sit inside content the model consumes: a document, a webpage, a support ticket, an email, retrieved context or another external source.
The AI system reads that content and may treat hostile text as an instruction. RedMaw tests whether untrusted content can change what the system reveals, ignores or attempts to do.
Current scope
Disclosure and jailbreak testing
- Sensitive-information disclosure
- System-prompt disclosure
- Jailbreaks
- Control bypass
- AI endpoint and model posture
- Prompt and response evidence
Findings map into the AI security taxonomy the product uses, including OWASP LLM and MITRE ATLAS context where supported.
Release gate
Make model changes a security event.
A retrain, fine-tune or model upgrade can change security behavior even when application code stays the same. RedMaw integrates adversarial testing into the CI or model-release pipeline, so a candidate version is:
- 01Triggered from the model registry or release process
- 02Tested with the adversarial suite
- 03Compared with the last accepted baseline
- 04Blocked from promotion if a relevant regression appears
- 05Preserved with a signed result the pipeline can consume
That makes AI security part of the release gate rather than a review that happens after deployment, if at all.
Black-box and release-pipeline testing
RedMaw supports black-box testing against model or AI-feature endpoints, and deeper testing in the release pipeline when the candidate version is available.
The practical result is one security loop across deployed behavior and pre-release model change.
Posture
What is exposed around the model
Active red-teaming covers behavior. Posture covers the deployment around it:
- Exposed or unauthenticated inference endpoints
- Unsafe serving configuration
- Model provenance
- Risky third-party models, datasets or plugins
Boundaries
Two things we will not claim
Evidence
What an AI finding preserves
- Target model or endpoint
- Version where available
- Attack class
- Adversarial input
- Model response
- The control that failed
- Reproducibility
- Remediation state and retest result
- Baseline comparison
That evidence also supports EU AI Act-oriented risk-management and robustness workflows for relevant systems.
What changes for your team
You can turn the answer to “do you red-team your AI?” from
We tested it once.
into
We run adversarial testing against the deployed system, and again when the model changes.
That is a stronger engineering answer, and a stronger enterprise-security answer.
Outcome
What changes for the AI / ML Lead
Not a longer list of possible problems. A shorter list of proven ones, and evidence that the fix held.
- Adversarial testing of deployed models and AI features
- Release-gate validation triggered by model change
- Comparison against the last accepted security baseline
- Prompt and response evidence preserved with each finding
- Model and endpoint posture alongside active red-teaming
- Evidence that supports AI governance workflows
- An answer to “do you red-team your AI?” that is not “we tested it once”
Other roles