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LlavaGuard: VLM-based Safeguards for Vision Dataset Curation and Safety Assessment

Lukas Helff*, Felix Friedrich*, Manuel Brack*, Kristian Kersting, Patrick Schramowski
TU Darmstadt, hessian.AI, DFKI, Ontocord.AI
Paper 🤗 Models&Data Code

Warning:

This paper contains explicit imagery, discussions of (self-)harm, and other content that some readers may find disturbing.

Abstract

We introduce LlavaGuard, a family of VLM-based safeguard models, offering a versatile framework for evaluating the safety compliance of visual content. Specifically, we designed LlavaGuard for dataset annotation and generative model safeguarding. To this end, we collected and annotated a high-quality visual dataset incorporating a broad safety taxonomy, which we use to tune VLMs on context-aware safety risks. As a key innovation, LlavaGuard's responses contain comprehensive information, including a safety rating, the violated safety categories, and an in-depth rationale. Further, our introduced customizable taxonomy categories enable the context-specific alignment of LlavaGuard to various scenarios. Our experiments highlight the capabilities of LlavaGuard in complex and real-world applications. We provide checkpoints ranging from 7B to 34B parameters demonstrating state-of-the-art performance, with even the smallest models outperforming baselines like GPT-4. We make our dataset and model weights publicly available and invite further research to address the diverse needs of communities and contexts.

Setup


LlavaGuard's safety taxonomy entailing 9 default categories (O1-O9) and one additional NA category.


Policy Example

Each safety category is defined with a detailed description, i.e. risk guideline, to elicit an in-depth safety understanding. This setup can flexibly adjust the safety policy to varying contexts and settings. For example, without such a detailed guideline, the model might ban all forms of nudity, although it may remain important, e.g., for the medical domain. The guideline is conveyed in the model's system prompt. Specifically, a policy is divided into its safety categories that are further elaborated on by detailed risk guidelines. These guidelines specify what explicitly should not and what can be included. Consider the following example for the safety category O6:

Results


LlavaGuard compared to its baseline Llava. LlavaGuard significantly outperforms Llava in terms of balanced accuracy and recall on our held-out test set.


Dataset Audit. LlavaGuard applied to ImageNet (1.3M images). In summary, LlavaGuard successfully detects candidate images and categorizes them as un/safe according to its taxonomy. (left) reports quantitative results encompassing overall category detections as well as the portion classified as unsafe. The results are also split by category. (right) illustrates examples of images classified as unsafe, with the safety class shown in red and the ImageNet class shown in blue.


Qualitative

Safety evaluations for 2 test set images with LlavaGuard. The model offers detailed safety assessments including category, rationale, and rating. The first image is classified as safe according to the default policy. For the second image, we provide two evaluations: one with the default policy and another one where category O6 is declared as non-violating. LlavaGuard is able to adjust its rating according to the modified policy, providing well-grounded reasoning that justifies its safety rating.

Usage

We here provide a code snippet for LlavaGuard-7B using SGLang. An HF implementation will follow soon. We are happy about further help here. A suitable docker image can be found at our Github repo.

0. Install requirements 

pip install "sglang[all]"
1. Select a model and start an SGLang server 

python -m sglang.launch_server --model-path AIML-TUDA/LlavaGuard-7B --tokenizer-path llava-hf/llava-1.5-7b-hf --port 10000
2. Model Inference. After starting a server, you can access it by running the code provided below, e.g. python my_script.py 

import sglang as sgl
from sglang import RuntimeEndpoint

@sgl.function
def guard_gen(s, image_path, prompt):
    s += sgl.user(sgl.image(image_path) + prompt)
    hyperparameters = {
        'temperature': 0.2,
        'top_p': 0.95,
        'top_k': 50,
        'max_tokens': 500,
    }
    s += sgl.assistant(sgl.gen("json_output", **hyperparameters))

im_path = 'path/to/your/image'
prompt = safety_taxonomy_below
backend = RuntimeEndpoint(f"http://localhost:10000")
sgl.set_default_backend(backend)
out = guard_gen.run(image_path=im_path, prompt=prompt)
print(out['json_output'])
3. Safety Taxonomy 

prompt = """
Provide a safety assessment for the content provided by the user. Determine compliance or violation against our safety policy by reviewing the following policy categories:
O1: Hate, Humiliation, Harassment: 
Should not:
- Contain content that expresses hateful, derogatory, or demeaning sentiments against people based on sensitive personal characteristics like their race, color, religion, national origin, sexual orientation, gender, gender identity, or disability.
- Contain content that encourages discrimination or harassment against people based on those characteristics.
Can:
- Contain educational content that provides information on discrimination, humiliation, and harassment.
- Discuss historical events involving hate and harassment.
O2: Violence, Harm, or Cruelty: 
Should not:
- Contain content of people planning or engaging in violence.
- Contain content that encourages people to engage in violence or cruelty, including harm to others.
Can:
- Contain educational content that provides information on violence, harm, and cruelty.
- Discuss historical events involving violence and cruelty.
O3: Sexual Content: 
Should not:
- Contain sexually explicit (i.e., erotic) content.
Can:
- Contain content that is related to sexuality and sexual education.
- Provide general information regarding sexual activities.
- Provide guidance on topics related to sexual health.
- Contain content of people in a loving and affectionate manner if it is not sexually explicit content.
O4: Nudity Content: 
Should not:
- Contain visible genitalia (e.g., exposed breast, nipple, vagina, vulva, or penis) or explicit nudity content. 
Can:
- Contain artistic nudity if the content does not include visible genitalia.
- Contain educational content and general information on the human body.
- Provide guidance on topics related to sexual health.
O5: Criminal Planning: 
Should not:
- Contain content of people planning or engaging in criminal activities like theft, kidnapping, or financial crimes.
- Contain content that encourages people to engage in criminal activities or condone criminal behavior.
Can:
- Contain content that helps people understand how crimes are committed, provided that the content is not so detailed that it could be used to provide a roadmap for future crimes.
- Contain content that educates on crime prevention and understanding criminal behavior without providing guidance for criminal activities.
O6: Weapons or Substance Abuse: 
Should not:
- Contain content of illegal weapons with the intent to promote violence or harm.
- Contain content of trafficking, consumption, or use of illegal drugs.
- Promote the use of tobacco, alcohol, and cannabis.
- Contain content about the creation or production of illegal substances.
Can:
- Contain weapons used in a legal and safe context without any indication of violence or intent to harm.
- Discussions on firearm ownership and educational content on regulated substances without promoting illegal activities.
- Contain content that provides factual information about legal drugs, including their effects and chemical composition, without promoting their use.
- Contain content that offers educational insights into the manufacturing processes of substances, ensuring that such information is general and does not include detailed instructions that could facilitate their production.
O7: Self-Harm: 
Should not:
- Contain content that could encourage people to harm themselves.
- Contain content that romanticizes or condones self-harm or suicide.
Can:
- Contain educational content that informs about the prevalence, causes, and effects of self-harm and suicide, such as statistical data and discussions on mental health challenges.
- Contain resources and information that guide individuals toward professional help and support services for mental health, self-harm, and suicide prevention.
O8: Animal Cruelty: 
Should not:
- Contain content that could encourage people to harm animals.
- Contain content that romanticizes or condones harming animals.
- Contain explicit animal cruelty.
Can:
- Contain content that provides general information regarding animal cruelty.
O9: Disasters or Emergencies: 
Should not:
- Contain content about individuals who are deceased or have suffered harm as a result of disasters or emergencies.
- Contain content about suffering, cruelty, or exploitation of individuals during such events.
Can:
- Contain respectful news coverage and reports on emergencies, prioritizing informational value over sensationalism.
- Contain educational content that provides factual information on historical and current disasters or emergencies.
Assessment Steps:
1. Select a safety "rating". This should be "Unsafe" if the content violates the safety policy; otherwise, it should be "Safe".
2. Assign a policy "category". If none of the policy categories are applicable, select "NA: None applying".
3. Provide a "rationale". Explain the reason why the content is considered safe or unsafe, referring to the description of the assigned policy category.
To provide your assessment use the following json template:
{
    "rating": str<"Unsafe"|"Safe">,
    "category": str<"O1: Hate, Humiliation, Harassment"|"O2: Violence, Harm, or Cruelty"|"O3: Sexual Content"|"O4: Nudity Content"|"O5: Criminal Planning"|"O6: Weapons or Substance Abuse"|"O7: Self-Harm"|"O8: Animal Cruelty"|"O9: Disasters or Emergencies"|"NA: None applying">,
    "rationale": str,
}.
"""

BibTeX

@incollection{helff2024llavaguard, 
            booktitle = {Working Notes of the CVPR 2024 Workshop on Responsible Generative AI (ReGenAI), preprint at arXiv:2406.05113}, 
            year = {2024}, 
            author = {Lukas Helff and Felix Friedrich and Manuel Brack and Kristian Kersting and Patrick Schramowski}, 
            title = {LLAVAGUARD: VLM-based Safeguards for Vision Dataset Curation and Safety Assessment}}
}