OpenAI o3-mini系统卡安全性与性能评估及风险分类

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OpenAI o3-mini系统卡是一项专注于评估OpenAI o3-mini模型在性能与安全性方面表现的研究成果。该模型基于大规模强化学习训练，具备链式思考推理的能力，能够更好地提升模型的安全性和稳健性。本文档详尽阐述了该模型如何在面对多种风险类别时作出反应，并提出相应的风险分类和评估框架。知识点详细说明： 1. 链式思考推理能力：链式思考是指在进行决策时，不仅仅依赖于单一的逻辑推演，而是形成一系列的思考链条，使得决策过程更加深入和全面。OpenAI o3-mini模型通过链式思考的训练，能够有效地理解复杂的指令和问题，并在执行任务时考虑到更多的上下文信息。 2. 安全性特征：OpenAI o3-mini模型在设计和训练过程中，特别强调了安全性。其通过预训练数据源的多样化和严格过滤，确保模型在面对恶意使用时具备稳健性。同时，模型还引入了特殊的安全机制，如遵循指令优先级和提高抵制有害提示的能力，以此来降低产生虚假内容的风险。 3. 性能评估：本文档对OpenAI o3-mini模型进行了性能评估，包括其对违规请求的拒绝能力、越狱尝试的抵抗能力、降低虚假内容产生可能性以及提升编码性能等方面。这些评估结果有助于了解模型在实际应用中的表现，并为未来的模型迭代提供理论依据。 4. 风险分类：文档中提到的风险分类包括化学/生物威胁创造、网络犯罪、自主性和说服力等。对于每一类风险，文档都提供了详细的风险评估和相应的防御措施。 5. 模型的稳健性：由于预训练数据的质量和多样性，以及在训练过程中注重稳健性，OpenAI o3-mini模型在面对现实世界的恶意使用时，能够展现出较强的适应能力和鲁棒性。 6. 技术应用场景：该文档的目标用户主要是AI研究和应用的技术人员，如机器学习工程师、研究员和技术管理者。文档提供的信息有助于这些专业人员制定部署策略，了解模型的性能和安全性，从而为业务决策和模型迭代提供支持。 7. 监管机构和利益相关方：本评估报告不仅适用于将要部署该模型的企业和个人开发者，同时也为监管机构和其他利益相关方提供了重要的参考信息。这有助于各方更好地理解和应对人工智能模型带来的潜在风险。 8. 评估框架和风险预测：文档中提及的“Preparedness Framework”和“SAG”（Safety Advisory Group）提出了将o3-mini模型的风险分类为中等风险，并建议采取特定的风险管理措施。这表明OpenAI在模型设计和风险控制方面采取了前瞻性和预防性的方法。 9. 性能与安全性的持续改进：文档强调了对模型性能与安全性评估的持续关注，并指出了可能存在的低估问题。这意味着OpenAI在模型的研究和应用中，持续关注模型的实际表现和潜在风险，不断寻求改进的机会。总体来说，OpenAI o3-mini系统卡为AI领域的研究者和实践者提供了一套综合的评估模型，既包括了模型的内部机制和技术细节，也包括了对外部风险的分类和管理建议。通过这种方式，OpenAI展现了其对大型语言模型安全性与实用性研究的重视，并为未来的AI应用和风险控制提供了重要的参考依据。

this allowed us to more easily parse through and assess prompts where o3-mini safety may be

perceived to be poorer than the safety of prior models.

We found that o3-mini performance was comparable to o1 on this cohort of requests, while both

o1 and o3-mini performed signiﬁcantly better than gpt-4o, as detailed in Table 9 showing Win

Rate

. Conversations were rated by the person generating the red teaming example, their peer

red teamers, and a third party data labeling company.

Table 9: Win Rate of o3-mini vs Comparable Models

Match-up (Winner vs Loser) Self Rated Win-Rate Peer Rated Win-Rate Third Party Rated Win-Rate

o3-mini over gpt-4o 73.3% ± 4.0% 83.1% ± 1.4% 82.4% ± 2.0%

o3-mini vs o1 51.9% ± 3.8% 50.4% ± 0.6% 49.9% ± 2.6%

o1 over gpt-4o 71.8% ± 2.8% 82.8% ± 1.3% 82.4% ± 2.1%

We ﬁnd that GPT4o refused less often than o1 and o3-mini on red teamer queries which further

corroborates the ﬁndings above. GPT4-o refused 34.2% of the queries while o1 and o3-mini

refused 63.5% and 56% of the queries respectively.

4.3.2 Jailbreak Arena

OpenAI collaborated with Gray Swan to make the o3-mini-near-ﬁnal-checkpoint model available

for jailbreaking in the Gray Swan Arena. on Jan 4th, 2025. The challenges in the arena focused

on testing for generation of illicit advice, extremism and hate crimes, political persuasion, and

self harm. An attack was considered successful if the completion both triggered the OpenAI

moderation API and passed a classiﬁer checking for "complete and actionable" steps in the

completion or the presence of harmful content.

Average user attack success rates (ASR) for o3-mini (3.6%) were comparable to o1-mini (3.7%)

and gpt-4o (4.0%) and higher than o1 (1.9%).

5 Preparedness Framework Evaluations

The Preparedness Framework is a living document that describes how we track, evaluate, forecast,

and protect against catastrophic risks from frontier models. The evaluations currently cover four

risk categories: cybersecurity, CBRN (chemical, biological, radiological, nuclear), persuasion, and

model autonomy. Only models with a post-mitigation score of Medium or below can be deployed,

and only models with a post-mitigation score of High or below can be developed further. We

evaluated OpenAI o3-mini in accordance with our Preparedness Framework.

Below, we detail the Preparedness evaluations conducted on o3-mini. Models used only for research

purposes (which we do not release in products) are denoted as “pre-mitigation,” speciﬁcally o3-mini

(Pre-Mitigation). These pre-mitigation models have diﬀerent post-training procedures from our

launched models and are actively post-trained to be helpful, i.e., not refuse even if the request

would lead to unsafe answers. They do not include the additional safety training that go into our

Win rates computed using the Bradley-Terry model, conﬁdence intervals computed at 95% CI

Not all the queries necessarily should be refused.

publicly launched models. Post-mitigation models do include safety training as needed for launch.

Unless otherwise noted, o3-mini by default refers to post-mitigation models.

We performed evaluations throughout model training and development, including a ﬁnal sweep

before model launch. For the evaluations below, we tested a variety of methods to best elicit

capabilities in a given category, including custom model training, scaﬀolding, and prompting

where relevant. After reviewing the results from the Preparedness evaluations, OpenAI’s Safety

Advisory Group (SAG)[

] recommended classifying the o3-mini (Pre-Mitigation) model as overall

medium risk, including medium risk for persuasion, CBRN, and model autonomy and low risk for

cybersecurity. SAG also rated the post-mitigation risk levels the same as the pre-mitigation risk

levels, to err on the side of caution.

To help inform the assessment of risk level (Low, Medium, High, Critical) within each tracked risk

category, the Preparedness team uses “indicator” evaluations that map experimental evaluation

results to potential risk levels. These indicator evaluations and the implied risk levels are reviewed

by the Safety Advisory Group, which determines a risk level for each category. When an indicator

threshold is met or looks like it is approaching, the Safety Advisory Group further analyzes the

data before making a determination on whether the risk level has been reached.

While the model referred to below as the o3-mini post-mitigation model was the ﬁnal model

checkpoint as of Jan 31, 2025 (unless otherwise speciﬁed), the exact performance numbers for

the model used in production may still vary depending on ﬁnal parameters, system prompt, and

other factors.

We compute 95% conﬁdence intervals for pass@1 using the standard bootstrap procedure that

resamples among model attempts to approximate the distribution of these metrics. By default,

we treat the dataset as ﬁxed and only resample attempts. While this method is widely used, it

can underestimate uncertainty for very small datasets (since it captures only sampling variance

rather than all problem-level variance) and can produce overly tight bounds if an instance’s pass

rate is near 0% or 100% with few attempts. We show these conﬁdence intervals to convey eval

variance, but as always, please note that all of our evaluation results can only be treated as a

lower bound of potential model capability, and that additional scaﬀolding or improved capability

elicitation could substantially increase observed performance.

5.1 Preparedness evaluations as a lower bound

We aim to test models that represent the “worst known case” for pre-mitigation risk, using

capability elicitation techniques like custom post-training, scaﬀolding, and prompting. However,

our evaluations should still be seen as a lower bound for potential risks. Additional prompting

or ﬁne-tuning, longer rollouts, novel interactions, or diﬀerent forms of scaﬀolding are likely to

elicit behaviors beyond what we observed in our tests or the tests of our third-party partners.

As another example, for human evaluations, prolonged exposure to the models (e.g., repeated

interactions over weeks or months) may result in eﬀects not captured in our evaluations. Moreover,

the ﬁeld of frontier model evaluations is still nascent, and there are limits to the types of tasks

that models or humans can grade in a way that is measurable via evaluation. For these reasons,

we believe the process of iterative deployment and monitoring community usage is important to

further improve our understanding of these models and their frontier capabilities.

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