RAG System Forensics & Memory Tampering Investigation (RAG-DFIR) Fundamentals

RAG System Forensics & Memory Tampering Investigation (RAG-DFIR) Fundamentals Training by Tonex

Modern AI systems depend on Retrieval-Augmented Generation, yet subtle failures often hide in the retrieval pipeline itself. This program equips practitioners to investigate vector databases, embeddings, and memory layers with a disciplined DFIR mindset. You will learn to detect poisoned documents, trace context manipulation, and validate chain-of-thought provenance across toolchains. Impact on cybersecurity is significant: compromised embeddings can launder malicious content past controls, and memory tampering can exfiltrate sensitive data or bias decisions. By mastering forensic techniques for RAG stacks, teams strengthen incident response, raise assurance, and harden AI-enabled operations against adversarial interference.

Learning Objectives

• Map end-to-end RAG architectures and trust boundaries
• Perform structured triage of retrieval malfunctions and hallucination clusters
• Analyze vector stores for drift, decay, and integrity issues
• Identify and contain embedding poisoning and model-steered retrieval bias
• Reconstruct RAG attacks from artifacts, logs, and prompts
• Apply governance playbooks that elevate AI assurance in production
• Strengthen incident response where cybersecurity risk concentrates in memory and retrieval layers

Audience

• Cybersecurity Professionals
• DFIR and Threat Hunting Teams
• AI/ML Engineers and MLOps Specialists
• Security Architects and Risk Managers
• Data Platform and SRE Engineers
• Compliance, Audit, and Assurance Leads

Course Modules

Module 1 – RAG DFIR Basics

• RAG components and data flows
• Trust zones and threat models
• Evidence sources and chain custody
• Incident triage for RAG failures
• Logging, telemetry, observability
• Metrics for retrieval health

Module 2 – Vector DB Forensics

• Index types and implications
• Write paths, compaction, snapshots
• Distance metrics and recall impacts
• Access control and multi-tenancy risks
• Artifact capture from vector stores
• Integrity checks and baselining

Module 3 – Embedding Drift Analysis

• Semantic drift vs. domain shift
• Versioning of encoders and vocab
• Distribution shift diagnostics
• Drift detectors and thresholds
• Rollback and compatibility plans
• Reporting drift to stakeholders

Module 4 – Poisoned Document Detection

• Payload signatures and triggers
• Prompt-linked canaries and traps
• Content provenance and watermarking
• Outlier and cluster inspections
• Retrieval set sanity checks
• Quarantine and purge procedures

Module 5 – Context Manipulation Tracing

• Prompt injection investigation steps
• Tool call and function trace review
• Conversation state and memory bleed
• Cross-session contamination checks
• Guardrail evasion pattern catalog
• Root cause correlation and timing

Module 6 – RAG Attack Reconstruction

• Timeline building from mixed logs
• Replay of retrieval and ranking
• Hypothesis testing with counterfactuals
• Impact analysis and blast radius
• Containment and recovery actions
• Post-incident controls and hardening

Outcome
Participants will be able to identify RAG failures, detect tampering, and prove or disprove adversarial interference across retrieval pipelines, vector databases, embeddings, and context memory—elevating confidence in AI-assisted operations.

Ready to investigate and harden your RAG stack with DFIR precision? Enroll with Tonex to equip your team with practical techniques, repeatable playbooks, and assurance methods that keep AI trustworthy in production.