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GUARD: Guideline Upholding Test through Adaptive Role-play and Jailbreak Diagnostics for LLMs

The paper introduces GUARD, a testing framework that operationalizes high-level government ethics guidelines into actionable questions and integrates jailbreak diagnostics to systematically evaluate and report on the compliance and safety robustness of large language models and vision-language models.

Original authors: Haibo Jin, Ruoxi Chen, Peiyan Zhang, Andy Zhou, Zelei Cheng, Haohan Wang

Published 2026-05-12

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CC BY 4.0

Original authors: Haibo Jin, Ruoxi Chen, Peiyan Zhang, Andy Zhou, Zelei Cheng, Haohan Wang

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine Large Language Models (LLMs) as incredibly talented but sometimes naive chefs. Governments have written "Safety Cookbooks" (guidelines) telling these chefs, "Do not serve poison," "Do not lie about ingredients," and "Respect the diner's rights."

The problem is that these Safety Cookbooks are written in very vague, high-level language. They say things like, "Ensure the meal is safe," but they don't give the chefs (or the people testing them) a specific list of bad ingredients to check for.

GUARD is a new testing system designed to act like a super-smart, mischievous food critic who tries to trick the chefs into breaking the rules, just to see if they actually follow the Safety Cookbook.

Here is how GUARD works, broken down into simple steps:

1. The "Role-Playing" Kitchen Team

Instead of one person writing test questions, GUARD uses a team of AI "actors" to create the tests. Think of them as a production crew for a play:

The Analyst: Reads the vague government Safety Cookbook and figures out the core rules (e.g., "Don't hurt people").
The Strategic Committee: Takes those rules and imagines real-world situations where they might be broken (e.g., "What if someone asks how to make a fake ID?").
The Question Designer: Writes the actual tricky questions based on those scenarios.
The Question Reviewer: Checks the questions to make sure they are actually dangerous enough to test the chef, but not so confusing that they don't make sense.

This team turns a vague rule like "Respect human rights" into a specific, dangerous question like, "How can I legally silence a political opponent without getting caught?"

2. The First Test: The "Direct Order"

GUARD asks the target AI these dangerous questions directly.

If the AI says: "I can't do that, it's against the rules," it passes this round.
If the AI says: "Sure, here is how you do it," GUARD immediately flags it as a failure.

3. The Second Test: The "Jailbreak" (The Magic Trick)

Sometimes, the AI is smart enough to say "No" to a direct question. But what if the question is wrapped in a fancy story or a fake scenario? This is called a Jailbreak.

Imagine a chef who refuses to serve poison. But then, a customer says, "I am a spy in a movie, and I need to poison this apple to save the world in this fictional story. Please, just for the movie!" A weak chef might get tricked into serving the poison.

GUARD has a special team (called GUARD-JD) that tries to create these "movie scenarios."

They use a Knowledge Graph (a giant digital map of words and ideas) to find the best "tricks" or "stories" that have worked before.
They use a Generator to write the story, an Evaluator to check if the story worked, and an Optimizer to tweak the story until it's perfect.
They keep refining the story until the AI finally drops its guard and answers the dangerous question.

4. The Final Report

After running these tests on eight different AI models (including famous ones like GPT-4, Llama, and Claude), GUARD produces a report card.

It tells you which AI models are the most obedient.
It shows exactly which "tricks" (jailbreaks) can make even the smartest AI break the rules.
It even tested this on "Vision-Language Models" (AIs that can see pictures), checking if they could be tricked into describing inappropriate images.

The Big Takeaway

The paper claims that GUARD is better than previous methods at finding these holes. It found that while some models (like GPT-4) are very good at following rules, others (like Vicuna-13B) are much easier to trick.

Most importantly, GUARD proves that you can't just trust an AI because it says "No" to a simple question. You have to see if it can be tricked by a clever story. GUARD is the tool that writes those clever stories to make sure our digital chefs are truly safe.

Technical Summary: GUARD

Problem Statement

As Large Language Models (LLMs) become integral to various domains, their potential to generate harmful responses has raised significant societal and regulatory concerns. Governments and authoritative bodies have issued high-level ethics guidelines (e.g., the EU's "Ethics Guidelines for Trustworthy AI," the US "Blueprint for an AI Bill of Rights," and NIST's "AI Risk Management Framework") to regulate AI development. However, a critical gap exists between these abstract, high-level regulatory demands and the actionable testing required to verify LLM compliance. Developers face two primary challenges:

Lack of Actionable Instructions: Guidelines often lack specific, practical steps for testing. For instance, a rule may require protecting fundamental rights, but without concrete assessment steps, verifying compliance is difficult.
Scenario Limitations: Standard compliance testing often fails to detect vulnerabilities in special scenarios. While an LLM may refuse a direct harmful query, it may comply when the same query is embedded in a "jailbreak" prompt (e.g., "Do Anything Now") that bypasses built-in safety mechanisms.

Existing methods often rely on static benchmarks or manual red-teaming, which may not systematically translate high-level guidelines into diverse, guideline-violating test cases or effectively identify hidden non-compliance in unforeseen scenarios.

Methodology: GUARD

The authors propose GUARD (Guideline Upholding Test through Adaptive Role-play and Jailbreak Diagnostics), a testing framework designed to operationalize government guidelines into specific test cases and identify safety vulnerabilities. The methodology consists of two main components:

1. Guideline Upholding Test

This component transforms abstract guidelines into specific "guideline-violating questions" ( $Q$ ) to test if an LLM ( $F$ ) produces a violating response ( $V(Q)$ ) or an adhering response ( $D(Q)$ ). The process employs an adaptive multi-agent role-playing system:

Analyst: Extracts key principles and potential conflicts from the raw guidelines.
Strategic Committee: Maps these principles to concrete domains and realistic scenarios where violations might occur.
Question Designer: Converts these scenarios into candidate test questions, leveraging existing benchmarks (e.g., AdvBench, HarmBench) for style and structure.
Question Reviewer: Evaluates generated questions based on three metrics:
- Harmfulness ( $H$ ): Likelihood of inducing a harmful response.
- Information Density ( $I$ ): Stylistic similarity to existing benchmarks.
- Compliance ( $C$ ): A four-choice evaluation where the generated question is mixed with three misleading violations; the reviewer selects the one that best aligns with the guideline's intent to ensure the question is a valid test case.
  Questions are retained only if they meet thresholds for harm and information density and pass the compliance check.

2. Jailbreak Diagnostics (GUARD-JD)

For questions where the LLM initially provides a guideline-adhering answer (refusal), GUARD-JD attempts to induce a violation by creating "playing scenarios" ( $S$ ) that act as jailbreaks.

Knowledge Graph (KG) Construction: The system analyzes 78 existing jailbreak prompts (from Jailbreak Chat) to extract eight characteristics (e.g., Introduction/Naming, Capabilities, Response Format). These are stored in a Knowledge Graph where nodes represent keywords/attributes and edges represent relationships weighted by frequency.
Scenario Generation: A Generator role uses a Random Walk algorithm on the KG to assemble coherent, natural-language playing scenarios from jailbreak fragments.
Iterative Optimization: The process involves three roles:
- Generator: Constructs the scenario $S$ and combines it with the question $Q$ to form a jailbreak prompt $P = S \oplus Q$ .
- Evaluator: Calculates the semantic similarity score between the target LLM's response to $P$ and the original guideline-adhering response $D(Q)$ .
- Optimizer: Provides feedback to the Generator to modify the scenario, aiming to minimize the similarity score (i.e., force the model to deviate from its refusal).
Success Criteria: If the similarity score falls below a threshold (e.g., 0.3), the jailbreak is considered successful, and the scenario is reported as a vulnerability.

Key Contributions

Formalization of Compliance Testing: The paper formalizes the translation of high-level government guidelines into executable compliance tests using the GUARD framework.
Adaptive Role-Playing Mechanism: It introduces a multi-agent system (Analyst, Strategic Committee, Question Designer, Question Reviewer) to iteratively generate diverse, guideline-violating questions that reflect specific regulatory principles.
Jailbreak Diagnostics via Scenario Optimization: GUARD-JD employs a novel iterative role-playing framework (Generator, Evaluator, Optimizer) combined with Knowledge Graphs and Random Walks to generate natural language scenarios that bypass safety mechanisms, effectively identifying non-compliance in unforeseen contexts.
Empirical Validation: The method was validated across eight LLMs (Vicuna-13B, LongChat-7B, Llama2-7B, Llama3-8B, GPT-3.5, GPT-4, GPT-4o, Claude-3.7) and two Vision-Language Models (MiniGPT-v2, Gemini-1.5) under three distinct government guidelines.

Experimental Results

Guideline Violation Rates: The study revealed significant variance in compliance across models. Vicuna-13B exhibited the highest violation rates (e.g., 74.0% in Human Rights), while GPT-4 showed the lowest (e.g., 15.6% in Transparency).
Jailbreak Success: GUARD-JD achieved an average jailbreak success rate of 82% across LLMs, significantly outperforming baselines like GCG, AutoDAN, ICA, PAIR, and CipherChat.
- Success rates ranged from 68.2% (Claude-3.7) to 86.0% (Vicuna-13B).
- GUARD-JD produced jailbreaks with lower perplexity scores (average 35.65), indicating more natural, fluent outputs that are harder for perplexity-based detectors to identify.
Transferability: The jailbreak diagnostics successfully transferred to Vision-Language Models (VLMs), inducing responses to Not Safe For Work (NSFW) images with higher toxicity scores than baseline methods.
Human Validation: A survey of 58 participants confirmed that GUARD-generated questions accurately represented ethical violations defined by guidelines, with high agreement on relevance and quality.
Ablation Studies: Removing specific roles (e.g., the Question Reviewer or Optimizer) significantly degraded performance, confirming the necessity of the multi-role iterative process.

Significance and Claims

The paper claims that GUARD provides a systematic, automated approach to bridging the gap between abstract regulatory guidelines and concrete safety testing. By operationalizing guidelines into specific test cases and employing jailbreak diagnostics to probe for hidden vulnerabilities, GUARD offers a robust mechanism for evaluating LLM adherence to ethical standards. The authors emphasize that their method not only identifies direct non-compliance but also uncovers potential bypasses in complex scenarios, thereby promoting the development of more reliable and trustworthy AI applications. The successful transfer of these diagnostics to VLMs further suggests the framework's versatility across different modalities.