Securing an enterprise AI system requires moving beyond traditional firewalls.
AI Red Teaming and LLM Penetration Testing are the two pillars of AI
Resilience, ensuring that your models are not only secure from hackers but also safe, unbiased, and
compliant with global regulations like the EU AI Act and NIST AI RMF.
While traditional penetration testing checks for "broken locks" in code, AI security testing checks if the
"brain" of the application can be manipulated into making catastrophic mistakes.
Crafting complex jailbreak prompts to bypass safety guardrails and extract restricted or harmful outputs from AI models.
Identifying malicious or manipulated data injected into training sets or RAG pipelines that can bias model behavior.
Testing whether attackers can reverse engineer proprietary models or extract sensitive training data.
Evaluating models for toxic outputs, hallucinations, and biases that could lead to reputational or legal risks.
Large Language Models (LLMs) differ significantly in how they are built, deployed, and accessed. These differences directly influence their attack surface and risk exposure. Understanding these variations is critical for designing effective AI Red Teaming strategies and adversarial simulations tailored to each model type.
Proprietary, vendor-managed models accessed via APIs with limited internal visibility.
Common Deployment ModelSaaS / API-based platforms (e.g., OpenAI, Google, Anthropic)
Primary Attack SurfacePrompts, API endpoints, output channels
Typical Security RisksPrompt injection, data leakage, policy bypass, API abuse
i6 Red Teaming FocusAdvanced jailbreak simulation, output manipulation, sensitive data extraction
Models with full access to weights and architecture, deployed and managed by the organization.
Common Deployment ModelOn-prem, private cloud, hybrid cloud
Primary Attack SurfaceModel weights, training pipeline, inference endpoints
Typical Security RisksModel poisoning, weight tampering, unauthorized fine-tuning, insider threats
i6 Red Teaming FocusTraining data poisoning, model integrity attacks, inference abuse
Base models customized using proprietary organizational data for business functions.
Common Deployment ModelPrivate cloud or internal platforms
Primary Attack SurfaceFine-tuning datasets, prompts, internal integrations
Typical Security RisksBusiness logic abuse, data overexposure, cross-tenant leakage
i6 Red Teaming FocusBusiness workflow abuse, privilege escalation, data boundary testing
LLMs augmented with live data retrieval from internal or external knowledge bases.
Common Deployment ModelLLM + Vector DB + Document Stores
Primary Attack SurfaceVector databases, embeddings, retrieval logic
Typical Security RisksRAG poisoning, document injection, relevance manipulation
i6 Red Teaming FocusVector collision attacks, unauthorized retrieval, knowledge integrity testing
Autonomous or semi-autonomous LLMs capable of executing tools, APIs, or workflows.
Common Deployment ModelAI agents, copilots, automation engines
Primary Attack SurfaceTool execution layer, permissions, orchestration logic
Typical Security RisksPrivilege escalation, unsafe automation, transaction abuse
i6 Red Teaming FocusTool misuse simulation, agent chaining attacks, blast-radius analysis
Models that process text, images, audio, or video inputs simultaneously.
Common Deployment ModelCloud-based or enterprise AI platforms
Primary Attack SurfaceNon-text input channels, modality fusion logic
Typical Security RisksHidden prompt injection, cross-modal data leakage
i6 Red Teaming FocusImage/audio-based prompt injection, cross-modal attack testing
i6 AI Red Teaming is designed to emulate how real-world attackers, malicious insiders, and abuse-driven users attempt to exploit Large Language Models (LLMs) in production environments. Rather than limiting assessments to basic prompt testing, i6 conducts full-spectrum adversarial simulations across prompts, data pipelines, retrieval systems, APIs, agent workflows, and governance controls. Each engagement is tailored to the organization’s AI architecture, business context, and regulatory exposure, ensuring that risks are evaluated based on actual impact, not theoretical weaknesses.
Our red teaming methodology integrates globally recognized AI security frameworks such as OWASP and NIST, while leveraging i6’s proprietary attack playbooks and automation harnesses. We simulate adversarial behavior across closed-source, open-source, fine-tuned, RAG-based, and agentic LLMs, producing measurable risk metrics, reproducible attack paths, and prioritized remediation guidance. The outcome is not just vulnerability discovery, but operational readiness, audit confidence, and AI system resilience.
Attempts to override system instructions and safety guardrails.
Attack TechniquesRole manipulation, multi-turn escalation, token smuggling, Unicode abuse
LLM Types CoveredClosed, Open, Fine-Tuned, RAG
Business ImpactPolicy bypass, unsafe responses, brand damage
Extraction of PII, credentials, source code, or training data.
Attack TechniquesIndirect prompts, inference attacks, context leakage
LLM Types CoveredAll LLM types
Business ImpactRegulatory violations, IP loss, legal exposure
Manipulation of knowledge sources used by the LLM.
Attack TechniquesMalicious document injection, embedding collisions, ranking abuse
LLM Types CoveredRAG-based LLMs
Business ImpactDecision corruption, misinformation, insider risk
Unauthorized learning of model behavior or logic.
Attack TechniquesQuery harvesting, differential response analysis
LLM Types CoveredClosed & Open LLMs
Business ImpactIntellectual property theft, competitive risk
Compromise of fine-tuning or retraining datasets.
Attack TechniquesLabel manipulation, backdoor triggers
LLM Types CoveredOpen & Fine-Tuned LLMs
Business ImpactPersistent model compromise, hidden logic flaws
Abuse of autonomous actions and tool execution.
Attack TechniquesPrivilege escalation, unsafe chaining, API misuse
LLM Types CoveredAgentic LLMs
Business ImpactFinancial fraud, system compromise
Overuse or manipulation of inference APIs.
Attack TechniquesThrottling bypass, cost-amplification attacks
LLM Types CoveredClosed & Enterprise LLMs
Business ImpactService disruption, cost overruns
Inducing biased or unethical outputs.
Attack TechniquesAdversarial framing, contextual pressure
LLM Types CoveredAll LLM types
Business ImpactReputational damage, audit failure
Forcing confident but incorrect responses.
Attack TechniquesAmbiguous prompts, contradictory contexts
LLM Types CoveredAll LLM types
Business ImpactPoor decision-making, loss of trust
Misalignment with AI governance requirements.
Attack TechniquesControl bypass, missing audit artifacts
LLM Types CoveredEnterprise AI systems
Business ImpactRegulatory non-compliance, audit findings
Isix is a modern cybersecurity company dedicated to protecting businesses from digital threats. With expert solutions, 24/7 monitoring, and proven strategies, we secure your future in a connected world.
