AI Red Teaming
GenAI systems fail in different ways than traditional apps. Our AI Red Teaming services systematically attack your LLMs, agents, and GenAI applications to uncover jailbreaks, prompt injection paths, data exfiltration, tool abuse, safety bypasses, and supply-chain risks before adversaries or regulators do.
System-Level View
We test not just the model, but the system around it: Prompts, tools, data connectors, plugins, identity, and infrastructure, including third-party models and platforms.
Real Exploit Paths
Jailbreaks, prompt injection, data leakage, and agent/tool abuse chained into concrete impact: unauthorized data access, unintended actions, policy bypass, and supply-chain exposure.
Risk & Compliance Alignment
Findings mapped to business impact and frameworks like NIST AI RMF, OWASP GenAI/LLM risks, and sector-specific regulations or internal AI governance policies.
Who This Is For
- Teams building or operating LLM-powered products, assistants, copilots, or workflow automations.
- Security, risk, and compliance leaders needing defensible AI safety assurance for boards, regulators, or customers.
- Platform and ML engineering groups integrating foundation models, proprietary models, datasets, and tools into critical workflows.
Red teaming tailored to GenAI systems
We threat-model your AI system as a whole: models, prompts, tools, data, identity, supply-chain, and governance. From there, we design adversarial campaigns that match your risk profile: sensitive data exposure, abuse of autonomous actions, integrity and safety violations, and more.
Why Choose Bugscale for AI Red Teaming?
AI security expertise combined with battle-tested offensive techniques and application security experience.
Model & Behaviour Expertise
Focused testing of model behaviour and safety: jailbreaks, harmful content, policy violations, and misalignment scenarios across your domains of concern.
System & Supply-Chain Focus
Prompt injection, cross-domain attacks, data exfiltration, and misuse of tools, plugins, third-party models, datasets, and CI/CD pipelines where real risk accumulates.
Hybrid Approach
Where helpful and feasible, we extend into a hybrid assessment, providing an in-depth review of your orchestrator / agent code and safety middleware, combined with AI system red teaming, mirroring our application security methodology.
Evaluation & Governance Ready
Outputs that plug into engineering and governance, with prioritized hardening plan, and mapping to your AI risk and compliance frameworks.
Need assurance across traditional layers too? Explore our Application Security Services, Penetration Testing, and Security Controls Audits.
AI Red Teaming Engagement Process
A structured process to deliver credible, repeatable evaluations of your AI systems without disrupting production.
Kick-off & Objectives
Business objectives, threat scenarios, in-scope systems, risk appetite, and communication channels agreed up front.
AI Threat Modeling
Model & system inventory, data sources, tools, autonomy level, third-party dependencies (AI-BOM), and trust boundaries. Define priority attack paths and misuse scenarios.
Environment & Access
Safe test tenants, API keys, evaluation sandboxes, seeded accounts, source code repository, and representative data under strict least-privilege and data minimization principles.
Baseline Evaluation
Smoke tests, policy sanity checks, and representative prompts to measure the current safety posture and tune attack strategies.
Adversarial Campaigns
Manual-first red teaming of jailbreaks, prompt injection, data exfiltration, tool/agent abuse, and misalignment scenarios, with early disclosure of critical issues.
Report, Hardening & Validation
Executive summary, exploit narratives, backlog-ready issues, guardrail, supply-chain and architecture recommendations, and a retest window to confirm fixes.
AI Red Teaming Deliverables
Clear, reusable outputs you can plug into engineering, risk, and governance workflows.
AI Threat Model & Attack Surface
Documented assets, trust boundaries, data flows, tools, third-party dependencies, and priority attack paths for your AI systems.
Findings & Exploit Narratives
Reproducible prompts, transcripts, evidence, and impact descriptions for each verified issue, mapped to AI-specific risk categories and your business context.
Evaluation Suite
Curated prompt sets, scenarios, and test harness guidance that can be integrated into CI/CD or offline evaluation pipelines for ongoing AI safety checks.
Hardening Plan & Validation
Guardrail, configuration, code and architectural changes prioritized by risk, plus a retest window to validate mitigations and update the audit trail.
AI Red Teaming — FAQ
We focus on systems where language models interact with real data and actions:
- LLM-powered applications: Chatbots, copilots, internal assistants
- RAG systems: Retrieval-augmented generation on internal or external corpora
- Autonomous and semi-autonomous agents: Workflow engines, orchestrators, and tool/plugin ecosystems
- Vendor or platform integrations: SaaS features, extensions, and APIs backed by LLMs
If you are unsure whether your system fits, share a short description and we will outline a sensible scope.
We design adversarial campaigns around realistic failure modes for AI systems:
- Jailbreaks and policy bypasses
- Prompt injection and cross-domain instruction attacks
- Data leakage and targeted data exfiltration
- Tool and agent abuse leading to unintended actions
- Supply-chain risks in third-party models, plugins, datasets, and CI/CD
- Integrity and safety violations specific to your business domain
Yes. Retrieval and data pipelines are a major focus:
- RAG specific risks: Malicious or poisoned documents, retrieval manipulation, and untrusted data sources feeding your models
- Abuse of untrusted web or third-party content (e.g., SEO poisoning, prompt-in-data)
- Poisoning risks in internal knowledge bases and fine-tuning datasets
Where in-scope, we also review training and fine-tuning pipelines for poisoning and supply-chain issues, including model registry and deployment workflows.
We aim for realistic testing with controlled blast radius:
- Preferential use of test tenants, sandboxes, and seeded accounts
- Least-privilege access to APIs, backends, and data sources
- Use of synthetic or minimized datasets where feasible
- Careful handling of prompts and inputs so sensitive data is not unnecessarily sent to external model providers
Approach and data handling are agreed up front so security, ML, and legal/compliance stakeholders are comfortable with the test setup.
Traditional tests focus on code, infrastructure, and configuration. AI red teaming adds failure modes specific to model behaviour and orchestration:
- System-level view across prompts, tools, data flows, identity, and model providers
- Attack chains built from jailbreaks, prompt injection, and tool abuse to real impact
- Evaluation of guardrails, safety middleware, and agent/orchestrator logic
- Optional hybrid assessments that combine AI red teaming with code and architecture review
Yes we can propose an evaluation suite you can reuse as part of our deliverables:
We provide curated prompts, scenarios, and test harness guidance that can be integrated into CI/CD, scheduled jobs, or offline evaluation pipelines, so you can re-run safety checks after model updates, prompt changes, or new tool integrations.
Yes. We map findings and recommendations to AI-focused frameworks and standards as needed, including NIST AI RMF, OWASP GenAI/LLM risk categories, and sector-specific obligations on demand.
This helps you demonstrate due diligence to boards, regulators, and customers while keeping the focus on practical technical improvements.
Start an AI Red Teaming Engagement
Stress-test your LLMs, agents, and GenAI applications against realistic adversaries and turn the results into durable defenses.