AI Security & Red Teaming

Identify vulnerabilities across the LLM application stack, from prompt interfaces to retrieval pipelines and model APIs.

• 1.Threat Modeling LLM-Integrated Applications10m
• 2.Reconnaissance Techniques for Production AI Systems10m
• 3.Mapping Data Flow and Trust Boundaries in RAG Architectures10m

Execute direct and indirect prompt injection attacks, including payload crafting for data exfiltration and privilege escalation.

• 1.Direct Injection: System Prompt Overrides and Context Manipulation10m
• 2.Indirect Injection: Poisoning RAG Sources and External Tool Calls10m
• 3.Multi-Step Attack Chains: Escalating from Prompt Leak to Remote Execution10m

Circumvent safety filters, content policies, and alignment constraints through encoding tricks, roleplay exploits, and adversarial prompts.

• 1.Bypassing Content Filters with Obfuscation and Token Smuggling10m
• 2.Roleplay Exploits and Hypothetical Scenario Jailbreaks10m
• 3.Automated Red Teaming: Adversarial Prompt Generation at Scale10m

Design layered security controls including input validation, output filtering, privilege separation, and monitoring to harden LLM systems against attacks.

• 1.Input Sanitization Pipelines: Semantic Filtering and Prompt Firewalls10m
• 2.Runtime Monitoring and Anomaly Detection for Adversarial Behavior10m
• 3.Building Secure-by-Design LLM Systems: Isolation, Least Privilege, and Audit Trails10m

Structured approaches to discover LLM vulnerabilities through systematic adversarial testing frameworks.

• 1.Red Teaming Frameworks for LLM Security10m
• 2.Attack Simulation and Exploit Development10m
• 3.Metrics, Reporting, and Risk Quantification10m
• 4.Purple Team Collaboration and Defensive Hardening10m

Implement runtime guardrails and content filtering to detect and block unsafe LLM outputs at inference time.

• 1.Response Classification and Policy Engines10m
• 2.Structured Output Validation with JSON Schema10m
• 3.Semantic Similarity and Drift Detection10m
• 4.Real-Time Toxicity and Bias Mitigation10m

Identify and mitigate adversarial data injection, model backdoors, and dependency vulnerabilities.

• 1.Training Data Poisoning Attack Vectors10m
• 2.Dependency and Model Supply Chain Vulnerabilities10m
• 3.Model Extraction and Intellectual Property Theft10m
• 4.Secure Model Deployment and Runtime Hardening10m

Deploy observability, anomaly detection, and automated response systems for production LLM applications.

• 1.LLM Observability and Audit Logging10m
• 2.Behavioral Anomaly Detection with ML10m
• 3.Automated Threat Response and Policy Enforcement10m
• 4.Incident Analysis and Forensic Reconstruction10m

Isolate agentic workflows with runtime sandboxes, policy engines, and network-level controls.

• 1.Sandboxing LLM Tool Calls and Code Execution10m
• 2.Policy Engines for Agentic Workflow Authorization10m
• 3.Network Isolation and Egress Filtering for Agent Workloads10m
• 4.Secrets Management and Credential Rotation for AI Pipelines10m

Implement continuous security testing pipelines for production AI systems with automated red teaming.

• 1.Automated Red Teaming Frameworks for Production10m
• 2.Shadow Deployment and A/B Security Testing10m
• 3.Behavioral Anomaly Detection and Drift Monitoring10m
• 4.Compliance Reporting and Security Audit Trails10m

Orchestrate comprehensive red team exercises combining prompt injection, model poisoning, and runtime exploits.

• 1.Designing Full-Spectrum Red Team Campaigns10m
• 2.Exploit Chaining Across Agent Architectures10m
• 3.Evaluating Defense Resilience Under Adversarial Load10m
• 4.Capstone: Banking Sector Multi-Agent Attack Simulation10m

Aligning red team findings with regulatory frameworks, audit trails, and board-level governance.

• 1.Mapping Security Controls to AI Regulatory Frameworks10m
• 2.Executive Risk Communication and Board Reporting10m
• 3.Incident Response Playbooks for AI Security Breaches10m
• 4.Building a Sustainable AI Red Team Practice10m

Design and deploy zero-trust frameworks for multi-cloud AI systems with cryptographic verification and policy enforcement.

• 1.Zero-Trust Principles for AI Workloads10m
• 2.Cryptographic Model Verification and Supply Chain Integrity10m
• 3.Runtime Threat Detection with AI-Native EDR10m
• 4.Advanced Incident Response for AI Breaches10m

Advanced techniques for tracking adversarial campaigns, attributing attacks, and building threat intelligence programs for AI systems.

• 1.Adversarial Campaign Tracking and Pattern Analysis10m
• 2.Attribution Techniques for AI-Targeted Attacks10m
• 3.Building Enterprise AI Threat Intelligence Programs10m
• 4.Cross-Domain Attribution Case Study: Operation LLM Shadow10m

Execute a comprehensive red team engagement against a simulated enterprise AI deployment.

• 1.Capstone Scenario Brief and Threat Modeling10m
• 2.Multi-Vector Attack Execution and Documentation10m
• 3.Defense-in-Depth Architecture Redesign10m
• 4.Executive Risk Briefing and Remediation Roadmap10m