Introducing Sora: OpenAI’s Advanced Video Generation Model
Sora represents a groundbreaking leap in AI-driven video creation, integrating OpenAI’s expertise in text-to-video technology, animation, and storytelling tools. Designed with creativity and user engagement in mind, Sora allows creators to produce high-quality videos up to 1080p resolution with a maximum duration of 20 seconds.
With Sora, users can:
- Generate videos from text descriptions.
- Enhance, remix, and animate existing images or videos.
- Explore community creations for inspiration through Featured and Recent feeds.
Sora draws on OpenAI’s successes with DALL·E and GPT models, merging capabilities to unlock new storytelling opportunities while prioritizing safety and ethical deployment.
Table of Contents
The Core of Sora’s Innovation
Sora is built as a diffusion model, a method where videos are generated from noise and refined iteratively into coherent frames. By leveraging a transformer architecture, the model scales effectively, ensuring consistent quality across sequences. This innovation allows Sora to:
- Maintain subject continuity even when objects temporarily leave the frame.
- Create smooth and visually appealing motion.
One standout feature is its recaptioning technique, inherited from DALL·E 3, which uses highly descriptive captions during training to ensure Sora follows text instructions with exceptional accuracy.
Key Features and Applications
- Text-to-Video Creation: Turn written ideas into vivid, dynamic videos.
- Image Animation: Bring static images to life with detailed animations.
- Video Extensions: Seamlessly fill in missing frames or extend existing video content.
- Community Inspiration: Engage with user-generated content for creative ideas.
Sora provides a platform for creators to experiment with visual storytelling, simulate real-world scenes, and enhance creative expression like never before.
Addressing Risks and Ensuring Safety
Recognizing the potential for misuse, OpenAI has implemented comprehensive safety measures for Sora’s deployment. Drawing on lessons from DALL·E and ChatGPT, safeguards include:
- Mitigations against generating explicit or harmful content.
- Protections against misuse of likenesses and misinformation.
- Red teaming evaluations to identify vulnerabilities and refine safety protocols.
These measures ensure that Sora’s innovative features are leveraged responsibly, supporting a safe and constructive creative ecosystem.
With Sora, OpenAI is setting the stage for a new era in AI-powered storytelling, unlocking possibilities for creators while advancing the path toward artificial general intelligence (AGI).
Model Data and Training Overview for Sora
Sora’s development was heavily influenced by the success of large language models (LLMs), which have demonstrated the power of learning from internet-scale data. OpenAI adapted these principles for visual data, creating a model that can generate videos by learning from vast datasets containing diverse types of video and image content.
Visual Patches: The Core Representation of Sora
Whereas LLMs rely on text tokens to represent language, Sora uses visual patches to represent the visual elements in videos and images. These patches are smaller, localized sections of images and video frames that allow the model to efficiently learn spatial and temporal patterns in visual data.
How it works:
- Videos are first compressed into a lower-dimensional latent space to simplify the representation.
- The compressed video is then broken down into spacetime patches which capture both spatial (image) and temporal (motion) aspects of the content.
- These patches form the foundation of the model’s generative process, allowing Sora to scale effectively and learn from diverse video types.
Datasets Used for Training
Sora was trained on a diverse mix of datasets, which included both publicly available data and proprietary sources:
- Public Data: Industry-standard machine learning datasets and web crawls that provide a broad range of visual information.
- Proprietary Data: Data sourced through partnerships, including collaborations with companies like Shutterstock and Pond5 for AI-generated image and video content.
- Custom Data: OpenAI developed in-house datasets tailored to Sora’s specific needs.
- Human Data: Feedback and insights from AI trainers, red teamers, and internal employees, helping guide model improvements and safeguard quality.
Pretraining Filtering and Data Preprocessing
To ensure the safety and ethical deployment of Sora, the training data undergoes a pretraining filtering process to remove unwanted or harmful content before the model begins learning. This filtering helps minimize risks such as:
- Explicit or violent content
- Sensitive symbols or hate speech
- Misinformation or other harmful media
The filtering process is an extension of the techniques used in previous OpenAI models, such as DALL·E 2 and DALL·E 3, and ensures that only appropriate data is used during the training phase. This mitigative layer plays a critical role in reducing the model’s exposure to harmful or undesirable information, aligning with OpenAI’s commitment to safe AI development.
Conclusion
By leveraging diverse datasets, visual patches for scalable learning, and robust data preprocessing techniques, Sora is able to generate high-quality videos while maintaining ethical safeguards. These foundational strategies contribute to Sora’s potential as a transformative tool for creative video generation, while prioritizing safety and responsibility in its deployment.
Risk Identification and Deployment Preparation for Sora
In developing Sora, OpenAI undertook a comprehensive process to evaluate potential risks, both in terms of misuse and the creative potential of the tool. The goal was to balance the innovative capabilities of the model with a strong commitment to safety, ensuring that the product serves as a powerful creative tool without enabling harmful uses.
Gathering Feedback and Evaluating Risks
After announcing Sora in February 2024, OpenAI engaged with hundreds of visual artists, designers, and filmmakers from over 60 countries. This feedback helped shape the model’s design and features to better serve the creative community.
Additionally, internal evaluations and collaboration with external red-teamers played a critical role in identifying potential risks and iterating on solutions. This collaborative approach allowed OpenAI to continuously refine the model and ensure that safety considerations were integrated at every stage of development.
Iterative Safety Approach
Sora’s safety stack builds on lessons learned from other OpenAI models like DALL·E and ChatGPT, but it also incorporates custom safety mitigations designed specifically for video generation. Given the unique nature of video content, OpenAI recognized the need for more nuanced safety strategies, particularly when context is crucial. Some of the key measures include:
- Age Gating: Access to Sora is restricted to users who are 18 or older to minimize the risk of harmful content being created or shared by minors.
- Likeness and Face-Upload Restrictions: To prevent misuse, especially the generation of misleading or harmful content, the ability to upload faces or likenesses for video generation is restricted.
- Conservative Moderation: At launch, there are more conservative thresholds in place for moderating prompts and uploads involving minors, ensuring that the model doesn’t inadvertently generate inappropriate or harmful content.
Ongoing Learning and Iteration
OpenAI is committed to an iterative approach to safety, recognizing that the full scope of potential risks can only be understood once the model is actively in use. By continuously monitoring user interactions and learning from real-world applications, OpenAI aims to refine its safety features, balancing the need for creativity with the responsibility of safeguarding against harm.
This ongoing process allows Sora to evolve in response to both user needs and emerging risks, ensuring that the model’s capabilities are harnessed in a positive, responsible manner.
Conclusion
Sora’s development emphasizes safety-first innovation, with thoughtful risk identification, user feedback integration, and proactive safety measures that aim to create a secure environment for creators. By working closely with industry professionals and continuously iterating on its safeguards, OpenAI seeks to maximize the creative potential of Sora while addressing the novel challenges posed by video generation technology.
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External Red Teaming for Sora
To ensure the safety and integrity of Sora, OpenAI partnered with a diverse group of external red teamers from nine countries. These experts were tasked with identifying vulnerabilities in the safety mitigations, testing the model’s ability to handle adversarial tactics, and exploring novel risks associated with the model’s new capabilities. This collaboration began in September 2024 and continued into December 2024, with red teamers testing more than 15,000 generations.
The red teaming process was critical in refining Sora’s safety systems, helping to identify potential weaknesses and provide valuable insights into how adversarial users might exploit the tool. These efforts focused on a range of risks, including the generation of violative content and evasion of safety measures, providing feedback on bias, performance, and overall system robustness.
Key Testing Areas and Findings
1. Testing Violative Content
Red teamers tested the model’s ability to handle a variety of violative and disallowed content, including:
- Sexual and erotic content
- Violence and gore
- Self-harm
- Illegal content
- Mis- and disinformation
Through these tests, red teamers explored various text-to-video generation techniques and media upload capabilities. They tested both straightforward prompts and more adversarial tactics to assess how well the system could prevent the generation of harmful material. This included attempting to bypass safeguards using suggestive language, metaphors, and creative prompt structures.
2. Media Upload and Modification Tools
Red teamers also tested Sora’s media upload feature using a wide variety of images and videos, including those featuring public figures. They explored how uploading existing content, such as public images, could be used to generate violative content or deepfakes. Furthermore, red teamers tested Sora’s modification tools (e.g., storyboards, recut, remix, blend) to see how they could be used to create prohibited content. This testing revealed gaps in input/output filtering and highlighted the need for stronger safeguards in areas like media uploads and content modification.
3. Adversarial Tactics and Evasion
Red teamers also explored adversarial tactics to evade safety mitigations. This included experimenting with metaphors and suggestive language in prompts that could trigger content generation bypassing safeguards. Over time, red teamers were able to identify specific trends in wording and phrasing that circumvented the model’s protections. Some jailbreak techniques even proved effective in degrading safety policies, providing further data for refinement.
Key Observations and Insights
- Adversarial Prompting: Using medical situations or science fiction/fantasy themes sometimes allowed the model to bypass safeguards against generating sexual or erotic content. This insight led to the development of additional safety layers to prevent such evasion.
- Classifier Filtering: Testing revealed the need for stronger classifier filters to mitigate the risk of non-violative media uploads (e.g., innocent images) being modified into prohibited content like deepfakes or violent material.
- Tool Misuse: The storyboard, recut, remix, and blend tools were tested for their potential to generate harmful media, particularly when applied to images or videos of public persons. This testing helped identify vulnerabilities in input/output filtering, which were strengthened ahead of Sora’s public release.
Refining Safety Measures
The feedback from red teamers directly influenced the development of additional safety mitigations for Sora. These included:
- Enhanced prompt filtering, including improved blocklists and classifier thresholds.
- Refinements to safety evaluations that strengthen the model’s ability to reject harmful or malicious content across a broader range of content categories.
These insights allowed OpenAI to enhance Sora’s compliance with safety goals and improve its overall robustness, ensuring that the model remains a safe and reliable tool for creative professionals while preventing misuse.
Conclusion
The red teaming process was an essential step in ensuring that Sora is both a powerful creative tool and a safe product. By testing the model under real-world adversarial conditions and refining safety features based on red team feedback, OpenAI has significantly improved the robustness of Sora’s safety stack, paving the way for responsible deployment. This iterative process continues to enhance the system, balancing user creativity with safeguards to mitigate potential harm.
Learnings from Early Artist Access
Over the past nine months, OpenAI has closely monitored user feedback from the Early Artist Access program, which included over 500,000 model requests from 300+ users across 60+ countries. This engagement provided valuable insights that informed the development and refinement of Sora‘s features, behavior, and safety protocols.
Key Insights and Enhancements
1. Feedback on Watermarking and Flexibility
One major takeaway from the early artist access was the impact of visible watermarks on artists’ workflows. Artists reported that the watermark interfered with the professional quality and presentation of their work, especially for those creating content intended for commercial or public release. This feedback led to an important change: OpenAI decided to allow paying users to download videos without the visible watermark, while still embedding C2PA (Content Authenticity Initiative) data for traceability and content authenticity. This adjustment ensured that artists could use the model in a more flexible and professional manner while maintaining accountability and content verification.
2. Expanding Creative Flexibility
Sora’s role as a tool for storytelling and creative expression required a different approach to handling sensitive content. Based on feedback, OpenAI recognized the need to offer more flexibility in how certain content is generated, particularly in areas where creativity and artistic vision might require exploring complex or nuanced themes. This flexibility contrasts with the more restrictive safety protocols applied to general-purpose tools like ChatGPT, where a more cautious approach to sensitive topics is necessary. By allowing more creative freedom, Sora can better serve professional creators, independent filmmakers, studios, and entertainment organizations who are likely to rely on it as an essential part of their production processes.
3. Identifying Positive Use Cases vs. Potential Misuse
Through early testing and feedback, OpenAI also identified positive use cases where Sora was an invaluable tool for creators, helping them to generate content that pushed artistic boundaries and enabled new forms of storytelling. At the same time, the early access revealed potential misuse risks, particularly in areas like the creation of misleading or harmful content, which could involve deepfakes, violence, or other harmful depictions. This helped OpenAI pinpoint areas where more restrictive product-level mitigations were necessary to ensure the model’s responsible use, especially in preventing harm or unethical content creation.
Impact on Safety Protocols and Product Development
The insights gained from the early artist access program directly informed Sora’s ongoing product development and safety improvements. Key areas of focus included:
- Balancing creative flexibility with strong safety measures to prevent harmful or unethical content generation.
- Refining content moderation to address specific risks that might arise in creative industries while still enabling artistic freedom.
- Iterating on user-facing features, such as watermarking and download options, to meet professional demands without compromising safety.
Conclusion
The Early Artist Access program has been instrumental in shaping Sora’s development, helping OpenAI better understand the needs of creative professionals while ensuring that the tool remains safe and responsible. The feedback from artists, filmmakers, and other industry users has driven key adjustments to both product features and safety protocols, allowing Sora to serve as a powerful, flexible, and secure tool for creative expression. As Sora continues to evolve, OpenAI remains committed to learning from users and iterating on the product to strike the right balance between innovation and responsibility.
Evaluations for Sora
OpenAI developed a comprehensive evaluation framework to address key risk areas and refine safety measures in Sora. These evaluations focused on nudity, deceptive election content, self-harm, and violence—high-risk areas where the model’s output could potentially be harmful or unethical. The framework was designed to support the refinement of safety mitigations and help inform appropriate moderation thresholds.
Evaluation Methodology
The evaluation process involved a combination of input prompts and input/output classifiers to assess the model’s ability to handle sensitive content safely. The methodology includes:
- Input Prompts: These are the text or media inputs provided to the model, which are carefully selected to test the model’s responses in areas where risks are highest.
- Classifiers: Both input classifiers (which analyze the prompts) and output classifiers (which evaluate the generated videos) were used to identify potentially harmful or violative content at various stages of the process. This multi-layered approach helps catch issues both before and after the video is generated.
Sources of Evaluation Data
The input data for the evaluations came from three primary sources:
- Early Alpha Phase Data: This data was collected from real-world usage scenarios during the early access program. It provided insight into how users interact with the model and the types of content they generate, helping to identify common risks or issues that may arise in everyday use.
- Red-Team Contributions: Red team testers played a crucial role in identifying adversarial and edge-case content. Their feedback, as detailed in Section 3.1, highlighted areas where users might attempt to evade safety measures or produce harmful content through creative prompts or manipulative techniques. Their contributions were particularly valuable in testing the model’s robustness against evasion tactics and ensuring that safety measures were comprehensive and effective.
- Synthetic Data Generated Using GPT-4: In addition to real-world and red-team data, synthetic data was generated using GPT-4 to create evaluation sets for edge cases, particularly in areas like unintended racy content where real-world examples are less common. This approach allowed OpenAI to expand the evaluation sets and cover a wider range of potential risks that might not be fully represented in natural data.
Key Evaluation Areas
The evaluations focused on some of the most high-stakes content areas, including:
- Nudity: Ensuring that the model does not produce or generate inappropriate, explicit, or non-consensual nudity.
- Deceptive Election Content: Identifying and mitigating risks associated with the generation of misleading or manipulative content, particularly related to elections and political processes.
- Self-Harm: Preventing the model from generating content that could promote or encourage self-harm or suicidal behavior.
- Violence: Ensuring that the model does not generate graphic or harmful content related to violence, including gore, abuse, and harmful depictions.
Impact on Model Refinement
The insights from these evaluations allowed OpenAI to continuously refine the safety mitigations, focusing on areas where content generation could pose serious ethical or safety concerns. The evaluations also informed the development of moderation thresholds, helping to fine-tune the model’s ability to recognize and reject harmful content.
By using a combination of real-world data, red-team feedback, and synthetic examples, OpenAI ensured that Sora could be both powerful and safe, allowing creative professionals to use the tool effectively while maintaining a high standard of ethical responsibility.
Conclusion
The evaluation framework for Sora played a crucial role in identifying and addressing risks associated with harmful content. By leveraging a mix of real-world data, synthetic examples, and feedback from external experts, OpenAI was able to refine Sora’s safety protocols and moderation systems, ensuring the model’s outputs align with safety goals while supporting creativity and innovation.
Preparedness Framework for Sora
OpenAI has implemented a preparedness framework to evaluate the potential risks associated with Sora’s capabilities, focusing on areas where the model could have significant societal or ethical impact. The framework assesses four key risk categories: persuasion, cybersecurity, CBRN (chemical, biological, radiological, and nuclear), and model autonomy.
Risk Categories and Findings
- Cybersecurity, CBRN, and Model Autonomy
Based on the capabilities of Sora, OpenAI has determined that there are no significant risks in the areas of cybersecurity, CBRN, or model autonomy. These types of risks are typically associated with models that:- Interact directly with computer systems (e.g., hacking, malware generation).
- Engage with scientific or technical knowledge that could impact physical safety (e.g., chemical, biological, radiological, or nuclear risks).
- Have the ability to make autonomous decisions, which could lead to unintended actions or consequences.
- Persuasion Risks
Sora’s video generation capabilities could pose a persuasion risk, particularly in the realms of impersonation, misinformation, and social engineering. The ability to generate highly realistic videos opens up the possibility for these types of manipulation, where AI-generated content could be used to deceive or influence audiences. Specific risks include:- Impersonation of public figures or individuals.
- Creation of misleading or manipulative content that could spread false information.
- Social engineering attacks using AI-generated videos to deceive individuals into taking specific actions.
Mitigations for Persuasion Risks
To address the potential risks associated with persuasion, OpenAI has developed a comprehensive suite of mitigation strategies, which are implemented to ensure that Sora is used responsibly and ethically:
- Likeness and Public Figure Protection
OpenAI has put measures in place to prevent impersonation of well-known public figures. This includes specific safeguards to ensure that Sora cannot be easily used to create videos that mimic or impersonate public figures without authorization. - Multi-Layered Provenance Approach
Given that context and knowledge of whether a video is AI-generated are crucial in assessing its trustworthiness, OpenAI has built a multi-layered provenance approach into Sora. This approach includes:- Metadata: Embedding detailed information within the video files to indicate their AI-generated nature.
- Watermarks: Including visible or invisible watermarks that can clearly identify a video as being generated by Sora.
- Fingerprinting: Implementing unique digital fingerprints in the generated videos to track their origin and ensure authenticity.
These strategies aim to provide transparency around the origin of videos, helping viewers distinguish between real and AI-generated content, and mitigating the risk of using AI-generated videos for misleading or manipulative purposes.
Conclusion
The preparedness framework for Sora shows that while there are no significant risks related to cybersecurity, CBRN, or model autonomy, persuasion risks—such as impersonation, misinformation, and social engineering—are a valid concern. To mitigate these risks, OpenAI has implemented several safety measures including preventing impersonation of public figures, as well as a robust provenance system that ensures transparency and accountability for AI-generated videos. By addressing these concerns, OpenAI aims to ensure that Sora is used in a responsible and ethical manner while maximizing its potential for creative expression. Source at https://openai.com/index/sora-system-card/
Sora Mitigation Stack
OpenAI has implemented a robust mitigation stack for Sora to address the risks associated with AI video generation. This stack includes system-level and model-level technical mitigations as well as product policies and user education. The overall goal is to prevent harmful or unwanted content from being generated or shared, ensuring the safe and ethical use of the technology.
System and Model-Level Technical Mitigations
- Multi-Modal Moderation Classifier
Sora uses a multi-modal moderation classifier that is part of its Moderation API to assess whether the text, image, or video prompts may violate OpenAI’s usage policies. This classifier checks both input and output for potential issues, such as NSFW content, violence, and harmful misinformation. Violative prompts trigger a refusal response. - Custom Large Language Model (LLM) Filtering
Sora uses a custom LLM filtering system that performs moderation checks asynchronously without adding significant latency. During video generation (which takes a few seconds), the system runs precision-targeted checks for topics like deceptive content or third-party content issues. - Image Output Classifiers
Sora incorporates image output classifiers that scan generated videos for harmful content. These classifiers are specialized for:- NSFW content (Not Safe for Work).
- Content involving minors.
- Violence and other misuse of likeness.
- Blocklists
OpenAI maintains a set of textual blocklists based on prior work with DALL·E 2 and DALL·E 3, proactive risk discovery, and feedback from early users. These blocklists help prevent the generation of specific harmful content.
Product Policies
OpenAI has designed Sora’s product policies to prevent misuse and provide clear guidance on acceptable use:
- Age Restrictions
Sora is only available to users aged 18 and older. Content shown in the Explore and Featured feeds is subject to moderation filters to ensure appropriateness for a broad audience. - Prohibited Content Guidelines
OpenAI has clearly defined policies prohibiting:- Impersonation or misuse of likeness.
- Creation of illegal content or content that violates intellectual property rights.
- Explicit or harmful content, such as non-consensual intimate imagery, content promoting violence or hatred, and content designed to harass or defame.
- Disinformation, fraud, or scams.
- Moderation and Enforcement
OpenAI uses a combination of automated moderation and human review to actively monitor patterns of misuse. Violating content can be removed, and users who breach the guidelines may face penalties, including account bans.
Specific Risk Areas and Mitigations
- Child Safety
OpenAI places a high priority on child safety, implementing a series of measures to detect and prevent Child Sexual Abuse Material (CSAM). These include:- Integration with Safer by Thorn to detect and report CSAM.
- Multi-modal classifiers to detect content involving minors and enforce stricter moderation thresholds.
- Robust scanning of both first-party and third-party users’ inputs and outputs.
- Policies prohibiting the creation of sexual content involving minors.
- Nudity and Suggestive Content
Sora uses a multi-tiered moderation strategy to block the generation of NSFW or suggestive content, including:- Prompt transformations to modify inputs that may lead to inappropriate outputs.
- Image output classifiers to block potentially explicit content.
- Stricter moderation thresholds for content shown in the Explore section, ensuring it is appropriate for a wide audience.
- Deceptive Content
Sora’s moderation tools help prevent the generation of deceptive content, including:- Likeness misuse and harmful deepfakes.
- Deceptive election content, including videos that could be misleading in the context of political campaigns.
- Investments in Provenance
To ensure transparency and accountability, Sora integrates C2PA metadata, applies visible watermarks to generated videos, and uses an internal reverse video search tool. These tools help verify the origin of content and distinguish AI-generated videos from real ones.
Future Work and Iterative Deployment
OpenAI employs an iterative deployment strategy to ensure the responsible and effective roll-out of Sora. Planned developments include:
- Likeness Pilot: A cautious approach to the ability to generate videos using uploaded images or videos of real people. This feature will be tested with a subset of users, with active monitoring to assess its safety.
- Provenance and Transparency: Ongoing work to improve traceability, including research into reverse embedding search tools and partnerships with NGOs to improve the provenance ecosystem.
- Expanding Representation: Efforts to reduce potential output biases through continuous feedback and refinements in prompting and model adjustments.
Acknowledgements
OpenAI extends gratitude to its internal teams, red teamers, and early users who contributed to the development and evaluation of Sora’s safety mitigations. Special thanks to those who helped identify risks, test the models, and provide valuable feedback on the effectiveness of the system.
By integrating these technical mitigations, policies, and continuous improvements, OpenAI aims to ensure that Sora can be used creatively and responsibly while minimizing the risk of harmful or unethical content generation.
