8 MITRE ATT&CK-mapped attack scenarios — jailbreaks, prompt injection, model extraction, data exfiltration, and AI supply chain attacks — executed against your production AI systems.
Each engagement runs structured adversarial scenarios against your LLMs, RAG pipelines, and agent tool chains.
Systematic testing against persona injection, encoding bypass, multi-turn context poisoning, and adversarial suffix attacks to evaluate safety policy resilience.
LLM01Inject adversarial instructions directly and via poisoned RAG documents, tool outputs, and browsing content to test instruction-hijacking resistance.
LLM01Probe for model weight/architecture extraction via query-based distillation, membership inference, and systematic output sampling.
Model TheftTest whether crafted prompts can extract training data, system prompts, RAG-indexed confidential documents, or PII from model memory.
LLM06Assess third-party model weights, fine-tuning datasets, plugin marketplaces, and MCP tool servers for poisoning and backdoor risk.
LLM05Test agentic workflows for unauthorized tool execution, privilege escalation through chained actions, and unsupervised high-impact operations.
LLM08Structured, reproducible red team process from scoping to remediation validation.
Map your AI architecture — model providers, RAG pipelines, agent tools, MCP servers — and define attack scenarios aligned to your actual deployment.
Run the 8 MITRE ATT&CK-mapped scenarios against staging or production endpoints, with safe-mode guardrails to prevent unintended damage.
Confirm exploitability beyond proof-of-concept — does the jailbreak lead to actual policy violation, data leak, or unauthorized tool execution?
Deliver a prioritized findings report mapped to OWASP LLM Top 10 and MITRE ATT&CK, with specific architectural remediation guidance and re-test validation.
Traditional penetration testing assumes deterministic systems — a given input produces a predictable output, and vulnerabilities are reproducible exploit chains. AI red teaming operates against probabilistic, non-deterministic systems where the same prompt can yield different responses across runs, model versions, and temperature settings. This fundamentally changes both the testing methodology and the way findings must be reported.
We map every red team scenario to MITRE ATT&CK tactics adapted for AI — initial access (crafted prompts, poisoned documents), execution (tool invocation through injected instructions), persistence (conversation memory and RAG index poisoning), privilege escalation (chaining low-trust outputs into high-trust actions), defense evasion (encoding and obfuscation bypass of safety filters), and exfiltration (extracting training data, system prompts, or RAG-indexed secrets through crafted queries). This mapping lets security teams integrate AI red team findings into existing ATT&CK-based detection and response programs rather than treating AI risk as a separate, siloed category.
Our standard engagement covers eight scenario classes: jailbreak and safety policy bypass, direct prompt injection, indirect prompt injection via RAG and tool outputs, model extraction and distillation attacks, training data and PII exfiltration, AI supply chain compromise (poisoned fine-tuning data, malicious MCP servers, compromised model weights), excessive agency abuse in agentic workflows, and denial-of-service via resource exhaustion (prompt-based token flooding, recursive tool-calling loops). Each scenario is executed with multiple technique variants — a single jailbreak test is not representative; we run dozens of payload variations per category to establish a realistic resilience baseline.
Organizations that fine-tune proprietary models or build differentiated prompting strategies face a distinct risk: competitors or adversaries systematically querying the model to reconstruct its behavior, extract the underlying weights through distillation, or infer membership of specific training examples. We test query-based extraction resistance and recommend rate-limiting, output perturbation, and watermarking strategies where appropriate.
Modern AI applications rarely use a single, self-contained model. They compose foundation model APIs, open-source fine-tunes, vector databases, RAG retrieval pipelines, and an expanding ecosystem of MCP (Model Context Protocol) tool servers that grant agents capabilities like file access, code execution, and external API calls. Each link in this chain is a potential compromise point — a malicious MCP server can return poisoned tool results, a compromised fine-tuning dataset can embed backdoor triggers, and an unverified model checkpoint downloaded from a public hub can contain serialized code execution payloads. Our platform's MCP security scanner specifically assesses tool server trust boundaries as part of the broader AI red team engagement.
OWASP LLM08 (Excessive Agency) consistently produces our highest-severity findings. When an AI agent has standing permission to execute file operations, send communications, or process payments, a single successful injection in any upstream input — a webpage, an email, a support ticket — can cascade into real-world impact without any human in the loop. We test specifically for missing approval gates on irreversible actions and report these as critical findings regardless of how sophisticated the injection technique required to trigger them.
A point-in-time red team engagement establishes a baseline, but model providers update base models, your team ships new prompts and agent capabilities, and the attack technique landscape evolves weekly. We recommend treating AI red teaming as a continuous program — re-testing after model version changes, new tool integrations, or quarterly at minimum — and our platform supports scheduled re-assessment against your evolving AI attack surface, correlated with the same threat intelligence feed (Sentinel APEX, 1,625+ CVE advisories with CISA KEV and EPSS scoring) used across our broader security platform.
Reporting AI red team findings requires a different format than traditional penetration test reports. Because a single payload may succeed on one run and fail on the next against the same model and configuration, we report findings with success-rate statistics across repeated trials (e.g., "this jailbreak technique succeeded in 7 of 10 attempts") rather than a binary vulnerable/not-vulnerable determination. This gives engineering teams an accurate picture of real-world exploitability and lets them prioritize fixes for high-success-rate techniques over edge cases that succeed only under unusual conditions.
Testing adversarial scenarios against production AI systems carries inherent risk — a successful data exfiltration test could genuinely expose sensitive data, and an excessive-agency test against a live payment-processing agent could trigger a real transaction. Our engagements run under negotiated safe-mode constraints: exfiltration tests target canary data planted specifically for the engagement rather than genuine customer records, and high-impact agent actions are tested against staging environments or with destructive operations stubbed out, while still validating that the underlying authorization gap exists. This lets us test realistic attack paths without creating the exact incident the engagement is meant to prevent.
Traditional penetration testing assumes a relatively stable target — the same web application endpoint behaves consistently across test runs, and a confirmed SQL injection vulnerability remains exploitable until patched. AI red teaming operates against a target that changes underneath the tester: model providers silently update model weights, safety fine-tuning is periodically retrained, and the same payload that worked last month may be patched at the model-provider level without any action from your team. This means AI red team findings have a shorter shelf life than traditional penetration test findings and require more frequent re-validation to remain an accurate representation of current risk.
The highest-value AI red team engagements feed directly back into the development lifecycle rather than producing a report that sits unread after delivery. We work with engineering teams to convert confirmed findings into automated regression tests — a curated payload library specific to your application that runs in CI/CD against every change to system prompts, tool permissions, or RAG retrieval logic — so that a previously-fixed vulnerability cannot silently regress when a well-intentioned prompt change reopens it weeks later.
AI red team engagements can be scoped at different visibility levels depending on your goals. Black-box testing simulates an external attacker with no internal knowledge — only the public-facing chat interface or API — and is most representative of real-world adversary conditions. Grey-box testing provides the red team with documentation of system prompts and tool definitions without source code access, accelerating discovery of business-logic-specific injection vectors. White-box testing grants full access to source code, prompt templates, and orchestration logic, enabling the most thorough assessment of architectural weaknesses like missing privilege separation between model output and tool execution. Most mature engagements combine an initial black-box assessment with a white-box architecture review to catch both externally-discoverable and structurally-embedded risks.
A red team report that simply lists vulnerabilities by severity without actionable remediation guidance creates more work than it resolves. Every finding in our engagement reports includes the exact payload used, the model's response, screenshots or transcripts demonstrating impact, a root-cause explanation specific to your architecture (missing input sanitization, absent privilege separation, insufficient output filtering), and concrete remediation guidance mapped to your specific framework — whether that's LangChain, a custom orchestration layer, or direct API integration with a foundation model provider.
Because foundation models are updated silently by their providers and AI applications evolve rapidly through feature additions, a single annual red team engagement leaves long windows of unvalidated risk. We recommend a tiered cadence: lightweight automated regression testing on every deployment, a focused manual engagement after any significant architectural change (new tool integrations, a new RAG data source, a model provider switch), and a comprehensive engagement at least twice yearly covering the full attack surface including novel techniques that emerged since the prior assessment. High-risk deployments — agents with financial transaction capability, healthcare or legal advisory applications — warrant quarterly comprehensive engagements given the elevated impact of a successful compromise.
AI red teaming is relevant well beyond organizations building consumer-facing chatbots. Any organization deploying LLM-powered internal tools (code assistants with repository access, document summarization over confidential data), customer-facing applications, or autonomous agents with real-world action capability carries meaningful AI-specific risk regardless of company size. Regulated industries — financial services, healthcare, legal — face additional pressure given emerging requirements under frameworks like the EU AI Act and sector-specific guidance that increasingly expect documented AI security testing as part of broader risk management obligations.
Organizations frequently focus red team attention exclusively on customer-facing AI products while overlooking internal tools — an internal code assistant with repository write access, an internal document Q&A system indexed over confidential contracts and HR records, or an internal agent with access to production databases for troubleshooting. These internal deployments often have broader privilege and less external scrutiny than customer-facing products, making them an attractive target precisely because they're assumed to be lower risk. A comprehensive AI red team program scopes internal tooling with the same rigor applied to public-facing applications, since the blast radius of a compromised internal AI agent with database or deployment access can exceed that of a public chatbot leaking non-sensitive information.
Technical red team findings carry more weight when paired with a governance review that asks whether the right oversight existed in the first place — was this AI system's risk tier formally classified before deployment, did it go through any approval gate, and is there a documented owner accountable for its security posture. Pairing technical testing with this governance lens frequently surfaces systemic gaps beyond the specific vulnerabilities found, such as AI systems deployed entirely outside any formal review process — shadow AI that security teams didn't know existed until the red team engagement uncovered it during reconnaissance.
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