Introducing Safety Aligned DeepSeek R1 Model by Enkrypt AI
DeepSeek R1 has made waves in the AI industry, delivering high performance at a fraction of the training cost compared to existing LLMs. This marks a significant leap forward, especially for organizations struggling to justify the ROI of AI adoption. While the model excels in performance benchmarks, our red teaming uncovered critical security vulnerabilities that make it unsuitable for various use cases.
In our latest breakthrough, we leveraged SAGE [1], our state-of-the-art safety alignment data generation technique, to strengthen a distilled version model’s defenses against prompt injection and the generation of toxic or harmful content. Overall risk of the model was reduced by 47%. More details are available in the results section.
These advancements ensure that AI models like DeepSeek R1 can be both high-performing and safe for real-world deployment. The safety aligned deepseek-llama8b-model is available on Huggingface [2] for the community.
How We Did It?
Using Enkrypt AI Red Teaming, we identified vulnerabilities in the model and established baseline risk scores. These insights were then leveraged to generate a targeted safety alignment dataset—a crucial step in training the LLM to “say no” to unsafe or unethical queries. Our alignment data generation algorithm SAGE [1] is a taxonomy-driven synthetic data generation process that produces 51K in-depth prompts across 1,500+ harmfulness categories, enabling robust LLM safety training while maintaining benchmark performance. More advanced readers can read our research paper on SAGE [1] - our technique for safety alignment data generation.
The Results
Enkrypt Aligned DeepSeek-R1-Distill-Llama-8B showed a substantial decrease in risk after alignment. Toxicity of the model reduced by 57% while insecure code generation risk reduced by 77%. Risk of producing Harmful information like self harm, criminal planning or hate speech reduced by 99%. Risk of producing CBRN reduced by 69%. Overall Risk as defined by NIST framework decreased by 47%.
The alignment process also led to a slight increase in performance where the MMLU pro score of the model increased from 44.71 to 46.43.
To contribute to the AI community, we’ve shared the aligned DeepSeek R1 model on Hugging Face[2], ensuring accessible safety improvements for researchers and developers.
Comparison of Aligned Model with other LLMs
In our DeepSeek R1 red teaming report, we compared the model with gpt-4o, o1 and claude-3-opus[3]. The alignment performed on DeepSeek-R1-Distill-Llama-8B has increased the rank of DeepSeek R1 on our Safety Leaderboard from 69 to 12 which makes it safer than gpt-4o, o1-mini and claude-3-haiku. The Overall Risk of the Aligned model is almost similar to o1 with just a difference of 1%. Check our Safety Leaderboard for how aligned DeepSeek model compares against others [5].
For real world usage of the aligned DeepSeek R1 model, it can be paired up with Enkrypt AI Guardrails which can detect and block 99% of attacks, delivering one of the industry's best combinations of performance, cost efficiency, and safety. We are continuously working to make the model even safer by reducing bias and censorship.
A Callout to Model Providers
At Enkrypt AI, we’ve successfully reduced AI safety risks by up to 70% while preserving model performance. We invite other model providers to collaborate with us in aligning AI for safer deployment. If you’re interested in fortifying your models against security vulnerabilities and bias, let’s talk.
Links
[1] SAGE-RT: Synthetic Alignment data Generation for Safety Evaluation and Red Teaming
[2] Enkrypt AI Aligned DeepSeek R1 Huggingface
[3] DeepSeek Red Team Report by Enkrypt AI
[4] DeepSeek Under Fire: Uncovering Bias & Censorship from 300 Geopolitical Questions
[5] Enkrypt AI Safety Leaderboard
Frequently Asked Questions
LLM red teaming is adversarial testing that identifies security vulnerabilities in language models before deployment. It reveals hidden risks across toxicity, prompt injection, harmful content generation, and compliance gaps.
- Enkrypt AI red teaming covers 300+ risk categories systematically.
- Baseline vulnerabilities must be measured before safety alignment can succeed.
- Early detection prevents costly breaches and regulatory violations post-launch.
Safety alignment uses synthetic data generation to train models to refuse unsafe queries. Enkrypt AI's SAGE technique produces 51K targeted prompts across 1,500+ harmfulness categories, reducing overall risk by up to 47%.
- Red teaming identifies vulnerabilities; SAGE generates corrective training data.
- Toxicity risk dropped 57%, insecure code generation fell 77% in DeepSeek R1.
- Alignment maintains or improves benchmark performance during safety hardening.
Enkrypt AI's aligned DeepSeek-R1-Distill-Llama-8B ranks 12th on the Safety Leaderboard, outperforming GPT-4o, o1-mini, and Claude-3-haiku on overall risk. It matches o1's safety profile with just 1% risk difference.
- Harmful information risk (self-harm, hate speech, criminal planning) reduced 99%.
- CBRN generation risk decreased 69% after alignment.
- Benchmarked against 200+ LLMs on the public safety leaderboard.
Enkrypt AI's red teaming platform systematically evaluates open-source and frontier models across security, safety, and compliance dimensions. It identified critical vulnerabilities in DeepSeek R1 and generated the safety improvements now available on Hugging Face.
- Frontier model evaluation covers performance, toxicity, code injection, and CBRN risks.
- Results feed directly into safety alignment workflows and policy enforcement.
- Community models shared publicly to advance industry-wide AI safety standards.
Enkrypt AI's red teaming and safety alignment reduced DeepSeek R1's overall risk by 47%—the same approach works for your models. Book a demo to see how we identify and fix vulnerabilities in your deployments, or start a free trial today.
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