1. Audit Objectives

The objective of this methodology is to quantify and mitigate two primary failure modes in deployed Generative AI systems:

  1. Hallucinations: Providing factually incorrect, nonsensical, or ungrounded information presented as fact.
  2. Algorithmic Bias: Exhibiting demographic disparities, stereotyping, or toxicity based on protected axes (gender, race, religion).

2. Core Toolchain & Frameworks

  • Garak (Generative AI Red-teaming and Assessment Kit): An automated framework used for identifying vulnerabilities, prompt injection risks, and known failure modes in LLMs.
  • Promptfoo / LangChain Evals: Used to construct deterministic evaluation pipelines, measuring model outputs against expected baseline criteria.
  • Fairlearn / AIF360 (Optional for tabular ML): Traditional fairness metrics toolkits.

3. Auditing Phases

Phase 1: Baseline Establishment & Reconnaissance

  • Model Identification: Determine the underlying architecture, weights version, and system prompt configuration (e.g., Llama-3–70B, GPT-4, proprietary).
  • Use-Case Contextualization: Define the acceptable parameters for the model's domain (e.g., medical advice vs. creative writing). Context determines the strictness of hallucination thresholds.

Phase 2: Hallucination & Faithfulness Testing

  • RAG Grounding Verification: If using Retrieval-Augmented Generation, test the model's adherence strictly to the provided documents.
  • Factual Consistency: Feed the model adversarial or complex factual queries and use LLM-as-a-Judge (e.g., G-Eval) to score the output for factual accuracy against Wikipedia or verified truth corpuses.
  • Syber-System Hallucination Attacks: Use Garak modules (e.g., garak.probes.hallucination) to specifically trigger known generation failure loops.

Phase 3: Bias & Fairness Assessment (Demographic Parity)

  • Stereotype Amplification: Query the model with neutral prompts involving various demographics to observe disparate treatment or stereotyping in generation.
  • Toxicity Injection: Utilize the RealToxicityPrompts dataset or similar to measure the model's propensity to generate harmful content when provoked versus unprovoked.
  • Metric Extraction: Calculate disparate impact or equal opportunity scores based on prompt completion sentiment classification.

Phase 4: Jailbreak & Adversarial Robustness

  • Assess whether the model's safety guardrails preventing bias/hallucinations can be trivially bypassed using standard prompt injection tactics (e.g., DAN, Base64 encoding, persona adoption).

5. Remediation Strategy

  • System Prompt Hardening: Enforce strict grounding rules ("If you do not know the answer, state that you do not know").
  • Constitutional AI / Guardrails: Implement output filters using frameworks like NVIDIA NeMo Guardrails or Llama Guard to catch toxic or biased generations before they reach the user.
  • RLHF Fine-Tuning: Provide datasets emphasizing neutral, fact-grounded responses to align the model away from its biased latent representations.