RedCell AI Red Team
Red teaming for systems that talk, retrieve, and act
Traditional penetration testing was not designed for AI-enabled systems. AI introduces attack surface across prompts, models, RAG, embeddings, vector databases, memory, tools, plugins, APIs, identity, cloud, SaaS integrations, model supply chains, orchestration layers, MCP servers, A2A workflows, CI/CD, MLOps, and human approval paths. We test all of it — under written authorization, defined scope, and agreed rules of engagement.
More than jailbreaks
Jailbreak testing is one test class. We run more than thirty.
A vendor who equates AI red teaming with jailbreak prompts is testing the model's manners, not your organization's exposure. The findings that matter live in the connections: what the model can retrieve, what its tools can touch, whose identity it acts under, and what happens downstream when it is manipulated.
Shallow testing checks the model
Prompt-only testing measures whether the model says something embarrassing. Useful, bounded, and nowhere near sufficient.
Real exposure lives in the system
Retrieval layers, tool permissions, agent delegation, and identity boundaries are where manipulated output becomes unauthorized action.
Impact is measured in your terms
Every finding is mapped to business consequence — data classes exposed, actions available, obligations triggered — not just a severity color.
An AI attack path rarely ends at the model. Input becomes retrieval, retrieval becomes context, context becomes a tool call, and a tool call becomes an action in a real system with real permissions. Our assessments validate every hop — because attackers will.
Written authorization
No testing begins without signed authorization from the system owner. Ever.
Defined scope
Systems, environments, data, and techniques in and out of scope — documented and agreed.
Rules of engagement
Safety constraints, stop conditions, and escalation contacts enforced throughout.
Remediation-focused
The engagement ends when you can fix things — evidence, roadmap, retest, briefing.
Assessment catalog
Thirty-two modules across seven surfaces
Engagements are composed from the modules below — scoped to your architecture and risk objectives, not sold as a fixed bundle. Descriptions cover what we evaluate and why; technique details stay in the engagement, where they belong.
Discovery & Threat Modeling
AI Asset and Shadow AI Discovery
Builds an inventory of sanctioned and unsanctioned AI in your environment — applications, embedded vendor features, browser tools, and API usage — so testing and governance start from what actually exists, not what the architecture diagram claims.
AI Threat Modeling
Maps how an adversary would approach your specific AI systems: entry points, trust boundaries, data flows, and the business impact of each failure mode. Informed by MITRE ATLAS and the OWASP LLM Top 10, tailored to your architecture.
LLM & Application Testing
LLM Application Security Assessment
End-to-end assessment of an LLM-backed application: input handling, context construction, output handling, session boundaries, and the application logic wrapped around the model.
Prompt Injection Testing
Evaluates whether crafted user input can override system instructions, change application behavior, or reach functionality the design intends to withhold — and whether your layered controls catch it.
Indirect Prompt Injection Testing
Tests whether content the system retrieves or ingests — documents, web pages, email, tickets — can carry instructions that hijack model behavior. The attack surface most teams discover after deployment.
Jailbreak Resistance Testing
Measures how reliably safety and policy constraints hold under sustained adversarial pressure. One test class among more than thirty — useful signal, never the whole assessment.
RAG & Data Layer
RAG Security Testing
Assesses retrieval-augmented generation pipelines end to end: what can be planted in sources, what access control survives retrieval, and what the model will repeat to the wrong audience.
Vector Database Security Review
Reviews the configuration, access control, tenancy, and network exposure of vector stores — infrastructure that frequently ships with defaults chosen for demos, not production.
Embedding and Retrieval Manipulation Testing
Evaluates whether retrieval ranking and embedding behavior can be manipulated to surface attacker-chosen content or suppress the right answers.
Memory Poisoning Testing
Tests persistent memory features for durable contamination: whether an attacker can seed instructions or falsehoods that shape the system's behavior across future sessions and users.
Agents, Tools & Integration
Tool Abuse and Function Calling Security Testing
Assesses what a manipulated model can actually do with its tools: which functions can be invoked, with what parameters, under whose identity, and with what real-world consequences.
Agentic Workflow Security Review
Reviews systems that plan and act autonomously: goal handling, delegation, loop and budget limits, approval gates, and the blast radius when a step goes wrong at machine speed.
MCP Security Review
Security review of Model Context Protocol servers and clients: authentication, tool definitions, permission scope, prompt-carrying metadata, and the trust relationships between components.
A2A Workflow Security Review
Reviews agent-to-agent communication paths and protocols: how agents authenticate each other, what instructions they accept, and whether trust between them is earned or assumed.
Agent-to-Agent Abuse Testing
Controlled adversarial testing of multi-agent systems: whether one compromised or manipulated agent can steer, deceive, or escalate through its peers.
Plugin and Connector Security Testing
Evaluates third-party plugins and SaaS connectors attached to AI systems — the permissions they hold, the data they touch, and the pathways they open into adjacent systems.
Identity, Data & Escalation
API and Identity Boundary Testing
Tests the identity model around the AI system: whose credentials the model acts under, whether per-user authorization survives the AI layer, and where confused-deputy conditions exist.
Cross-System Escalation Testing
Assesses whether access gained through an AI system can be extended into surrounding infrastructure — cloud services, internal APIs, data stores — under controlled, scoped conditions.
Sensitive Data Disclosure Testing
Evaluates whether the system can be induced to reveal data it should withhold: other users' records, internal documents, credentials, system instructions, or regulated data classes.
Data Exfiltration Path Review
Maps the routes data could leave through an AI system — responses, tool calls, logs, embeddings, third-party calls — and validates the controls on each path.
Model & Supply Chain
Model Extraction and Model Theft Risk Assessment
Assesses exposure to model extraction and theft: API surface, rate and query controls, fine-tuned model storage, and the value at risk if the model or its behavior is replicated.
Model Denial of Service and Cost Abuse Testing
Tests resilience against resource exhaustion and cost amplification: whether an attacker can degrade service or run up your inference bill, and whether limits and alerts fire before finance notices.
Model Supply Chain Review
Reviews the provenance and integrity of the models you depend on — third-party APIs, open weights, fine-tunes — and the controls on how they enter and update in your environment.
AI-BOM Review
Builds or validates an AI bill of materials: models, datasets, frameworks, embeddings, and services underneath each AI system, so risk decisions rest on a real dependency picture.
Training Data and Fine-Tuning Risk Review
Reviews what goes into training and fine-tuning: data sourcing, sensitive content, poisoning exposure, and whether fine-tuned behavior undermines controls the base model enforced.
Detection, Response & Assurance
AI Guardrail Evaluation
Measures whether your guardrails — filters, classifiers, policy layers — hold under adversarial pressure, and where they can be bypassed, overwhelmed, or quietly disabled.
LLM Evaluation Harness Review
Reviews your evaluation pipeline: whether the tests you run before shipping actually measure the risks you care about, and whether regressions in safety behavior would be caught.
Runtime Logging and Observability Review
Assesses whether you could reconstruct an AI incident after the fact: prompt and completion logging, tool-call records, retention, privacy constraints, and gaps an investigator would hit.
AI Detection and Response Readiness
Evaluates whether your SOC would recognize an attack on an AI system — the signals available, the alerts defined, and the playbooks (if any) that mention the AI estate.
AI Incident Response Tabletop
Facilitated exercise for a realistic AI incident scenario — data disclosure through an assistant, agent misuse, poisoned retrieval — testing decisions, escalation, and communication before it is real.
AI Red Team Retesting
Focused re-validation after remediation: confirms fixes hold under the same adversarial conditions that produced the original findings, with a delta report suitable for leadership and auditors.
Continuous AI Security Validation Roadmap
Designs an ongoing validation program — cadence, scope rotation, regression suites, and triggers tied to system changes — so assurance keeps pace with an AI estate that changes weekly.
Scope of testing
What we test beyond the chatbot
The chat window is the least interesting part of the attack surface. A complete engagement examines the systems, identities, and pipelines the model is wired into.
- Identity and authorization
- APIs and tools
- RAG pipelines
- File ingestion
- Vector databases
- Embedding workflows
- Memory systems
- Model routing
- Prompt orchestration
- Agent frameworks
- MCP servers
- A2A protocols
- SaaS connectors
- Cloud storage
- CI/CD and MLOps
- Logging and telemetry
- Human approval paths
- Data classification
- Data retention
- Third-party model dependencies
- Open-source model risks
Methodology
Sixteen steps from authorization to assurance
The process is deliberately front-loaded: scope, context, inventory, and threat modeling before any adversarial work. Testing without understanding produces findings without meaning.
Step 1: Scope and rules of engagement
Written authorization, systems in and out of scope, safety constraints, environments, and escalation contacts — agreed before any testing begins.
Step 2: Business context and risk objectives
What the system does, who depends on it, and which outcomes would actually hurt. Testing priorities follow business impact, not novelty.
Step 3: AI asset inventory
Enumerate the models, applications, pipelines, tools, and integrations in scope — including the ones nobody mentioned in the kickoff.
Step 4: Architecture and data-flow mapping
Document how data and instructions move through the system: sources, context assembly, tools, outputs, and storage.
Step 5: Identity and permission review
Establish whose identity the system acts under at each hop and where privilege concentrates.
Step 6: Threat modeling
Develop the adversary's view: realistic attack paths ranked by feasibility and impact, mapped to MITRE ATLAS and OWASP LLM Top 10 classes.
Step 7: Abuse case development
Translate threats into concrete, testable abuse cases tied to business consequences and agreed with your team.
Step 8: Controlled adversarial simulation
Execute the abuse cases under the rules of engagement, with safety constraints and stop conditions enforced throughout.
Step 9: Guardrail and detection validation
Record which controls engaged, which failed, and what your monitoring saw — or didn't.
Step 10: Evidence collection
Capture reproducible evidence for every finding, packaged so defenders can verify and fix without guesswork.
Step 11: Business impact mapping
Connect each technical finding to operational, regulatory, financial, and reputational consequences.
Step 12: Risk-ranked reporting
Findings ordered by real exposure, written twice: an executive narrative and a technical appendix, from the same evidence.
Step 13: Remediation planning
A prioritized, practical remediation roadmap — owners, sequencing, and compensating controls where fixes take time.
Step 14: Retesting
Re-validate remediated findings under the original conditions and document what is closed versus still open.
Step 15: Executive briefing
A working session with leadership: what we found, what it means, what changed, and what to fund next.
Step 16: Optional training workshop
Transfer the attacker's perspective to your team so the next assessment finds less.
Deliverables
Reporting built for two audiences
Executives get a narrative they can act on. Defenders get evidence they can verify. Both come from the same engagement, and neither requires translating the other.
Executive AI red team briefing
A leadership-facing narrative of exposure, impact, and priorities — written for decisions, not drama.
Technical findings report
Full findings with reproduction evidence, affected components, and severity rationale.
AI attack surface map
A visual and written map of entry points, trust boundaries, and data paths across the assessed systems.
Threat model
The adversary-eye view of your architecture, reusable for future builds and reviews.
Abuse case library
The tested abuse cases, documented so your team can re-run and extend them.
Risk-ranked findings
Every finding ordered by exploitability and business impact — not by CVSS theater.
Evidence suitable for defenders
Logs, traces, and artifacts packaged for the people who have to fix and detect, not just admire the finding.
Remediation roadmap
Sequenced fixes with owners, dependencies, and compensating controls where needed.
Control mapping
Findings mapped to OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, and your internal control framework.
Retest report
Post-remediation validation: what closed, what remains, what changed.
Optional: secure architecture redesign
Design support to rebuild the weakest boundaries properly rather than patching around them.
Optional: tabletop exercise
An incident exercise built from your own findings — the most realistic scenario available.
Optional: custom training session
A private workshop teaching your team what the engagement taught us.
Findings mapped to
- OWASP GenAI
- OWASP LLM Top 10
- NIST AI RMF
- NIST GenAI Profile
- MITRE ATLAS
Framework references describe control mapping only and do not imply certification, endorsement, or partnership with any framework owner.
Discuss AI red teaming
Tell us what you have deployed — or are about to. We will scope an engagement that tests what actually matters, and we will tell you honestly if you are not ready for one yet.