17.3.2 Cross-Model Comparison

Why Compare? The Red Teamer’s Rationale

Standard performance benchmarks (like accuracy or F1-score) often mask critical security differences. Two models might achieve 99% accuracy on a task, but one could be trivially jailbroken while the other is robust. As a red teamer, your goal in cross-model comparison is to uncover these hidden disparities.

Effective comparison allows you to:

• Identify Relative Strengths and Weaknesses: Pinpoint which models are susceptible to prompt injection versus which are more prone to leaking private data.
• Reveal Systemic Flaws: If an attack works on three different models from different developers, you’ve likely found a fundamental vulnerability in the current training paradigm, not just a one-off bug.
• Inform Model Selection: Provide concrete security data to help stakeholders choose the most appropriate model for a given risk appetite.
• Guide Defensive Prioritization: If Model A is resistant to an attack that compromises Model B, you can study Model A’s architecture or fine-tuning data to develop better defenses.

Methodologies for Comparative Analysis

A fair comparison requires more than just running the same script against two different API endpoints. It demands a structured approach.

1. Baseline and Environment Normalization

Before you test, you must establish a level playing field. This is a direct extension of using standardized testing protocols. Ensure that confounding variables are minimized. Key considerations include:

• Identical System Prompts: Use the exact same meta-prompts or initial instructions for both models.
• Consistent Parameters: Lock settings like temperature, top_p, and max tokens to be identical across tests. A temperature of 0.9 on one model and 0.1 on another will produce incomparable results.
• Standardized Evaluators: Use the same automated scripts or human evaluation rubrics to judge the outputs from all models.

2. Differential Testing

This is the core of comparative analysis. You provide the exact same input to two or more models and analyze the differences in their outputs. This technique excels at highlighting subtle variations in behavior and safety training.

3. Vulnerability Profiling

Move beyond single scores. Create a comprehensive profile for each model that maps its performance across various attack categories. This provides a multi-dimensional view of its security posture, which is far more useful than a simple pass/fail metric. Visualizing this data, for instance with a radar chart, can make the relative strengths and weaknesses immediately apparent to stakeholders.

In the chart above, “Model Orion” is more vulnerable to jailbreaking but more robust against misinformation compared to “Model Vega”. This nuanced view is essential for making informed decisions.

4. Attack Transferability Analysis

A sophisticated technique is to test whether an adversarial prompt or payload crafted for one model works on another. This is attack transferability. High transferability is a red flag, suggesting that the vulnerability isn’t just a quirk of one model’s training run but a more fundamental weakness in a common architecture (e.g., the Transformer) or a shared dataset (e.g., Common Crawl).

Practical Example: Comparative Jailbreak Testing

Here is a simplified pseudocode example of how you might structure a comparative test for jailbreak resistance between two language models.

# --- Setup ---
import model_loader
import evaluator

# Load the models you want to compare
model_A = model_loader.load("proprietary-model-v4")
model_B = model_loader.load("open-source-llm-v2-chat")

# Load a standardized set of jailbreak prompts
jailbreak_prompts = open("jailbreaks.txt").readlines()

results = {}

# --- Execution Loop ---
for prompt in jailbreak_prompts:
    response_A = model_A.generate(prompt, temperature=0.5, max_tokens=500)
    response_B = model_B.generate(prompt, temperature=0.5, max_tokens=500)

    # Use a consistent evaluation function
    is_jailbroken_A = evaluator.check_for_refusal(response_A)
    is_jailbroken_B = evaluator.check_for_refusal(response_B)

    results[prompt] = {"Model_A_Jailbroken": is_jailbroken_A, "Model_B_Jailbroken": is_jailbroken_B}

# --- Analysis ---
print(results)

This simple harness ensures that each model faces the exact same challenges under the same conditions. The analysis phase then involves looking at the `results` dictionary to see which prompts were effective on one, both, or neither of the models.

Common Pitfalls in Comparison

Meaningful comparison is fraught with potential errors. Be vigilant for these common traps:

• The “Apples and Oranges” Fallacy: Comparing a 7B parameter model designed for summarization with a 175B model designed for chat is not a fair test of security. Always contextualize your findings based on model size, purpose, and architecture. Your conclusion shouldn’t be “Model A is better” but rather “For its size class, Model A shows higher resistance to X.”
• Benchmark Overfitting: If all the models you’re testing have been heavily fine-tuned on the same public safety benchmarks, they will likely share the same blind spots. Your comparison may show they are all equally robust to known attacks but equally fragile to novel ones.
• Metric Fixation: Do not declare a winner based on a single dimension. A model that perfectly resists jailbreaking might be easily manipulated into generating subtle, persuasive misinformation. The complete vulnerability profile is what matters.

Ultimately, cross-model comparison transforms your red teaming from a series of isolated tests into a strategic intelligence-gathering operation. By understanding how different models break, you gain insight into the entire ecosystem, allowing you to anticipate future threats and build more resilient defenses.