AETHERWARD

Service / 01

AI System Assessment & Red Teaming

Evidence-led assessment of models, copilots, RAG applications, agents, tools, and the infrastructure that gives them authority.

01
The objective

Test the whole AI system—not only the prompt—and turn credible failure paths into an ordered repair plan.

We assess the full operating environment around an AI capability: model behavior, system prompts, retrieval, memory, tools, identities, permissions, external services, deployment controls, telemetry, and human decision points. Testing is adapted to the system rather than reduced to a generic jailbreak checklist.

The work combines architectural review, attack-surface mapping, abuse-case development, controlled adversarial testing, and repeated evaluation where model variability matters. Findings include raw evidence, practical impact, uncertainty, and a clear remediation and retest sequence.

Often requested as

LLM penetration testingAI red teamingAgent security reviewAI security posture assessmentAI-SPM / AI-TRiSM review
Assessment surface

What we cover

  1. 01Prompt injection, jailbreak, and system-instruction abuse
  2. 02Agent, tool, MCP, memory, and multi-turn attack paths
  3. 03Sensitive-data disclosure and hidden-context exposure
  4. 04Excessive agency, privilege escalation, and unsafe action chains
  5. 05RAG, vector, embedding, and knowledge-base manipulation
  6. 06Supply-chain, model artifact, dependency, and integration risk
  7. 07Repeated and scenario-based testing for nondeterministic behavior
  8. 08Guardrail, policy, containment, and human-approval validation
The handoff

What you receive

  1. 01System-specific assessment and threat-surface map
  2. 02Risk-ranked findings with reproducible evidence
  3. 03Attack traces across prompts, tools, identities, and data
  4. 04Engineering remediation and compensating-control plan
  5. 05Leadership-ready risk summary and retest criteria

Designed outcomes

Known launch riskEvidence-backed findingsOrdered remediationDefensible release decision
How it works
01

Scope the system

Map components, data, identities, integrations, authority, and the decisions the system can influence or execute.

02

Model the abuse

Develop credible misuse cases and connected attack paths against the actual operating environment.

03

Test & evidence

Run controlled, repeatable tests and preserve traces, limitations, and uncertainty—not just pass/fail scores.

04

Repair plan

Prioritize fixes by practical risk, define compensating controls, and set clear validation gates.

Operating boundary: All work is performed within explicitly authorized scope. High-risk actions remain human-approved, and findings are communicated with evidence, uncertainty, and practical remediation context.

Research-driven security

Built for attack surfaces traditional security models were not designed to see.

Aetherward’s assessment methods are informed by continuous internal research into behavioral attack chains, legitimate-tool abuse, permission composition, cross-tool escalation, context manipulation, model-to-tool boundary failures, poisoning, and abnormal agent behavior.

Explore Aetherward research ↗
Additional capabilities

Fixed-scope or project-based

Security Automation Engineering

Custom security tooling for teams that need specialized automation without building a full internal platform.

Triage automationDetection toolingScope-aware scannersEvidence collectionThreat-intelligence workflowsAnalyst tooling

Recurring engagement

Ongoing AI Security & Architecture Advisory

Independent review as models, data, integrations, vendors, threats, and business requirements change.

Architecture reviewIntegration reviewThreat updatesRelease gatesSecurity driftDecision support

Building or deploying an AI system?

Assess it. Secure it. Build it. Repair it. ↗