DeepSeek-R1 Red Team Report: I found shocking security and moral risks

The RED team evaluation recently carried out by Enkrypt AI revealed the serious security risks, moral issues and vulnerability of Deepseek-R1. These discoveries have detailed the sensitivity of the model’s harmful, prejudice and unsafe content in the Report of the Red Team in January 2025, rather than the industry-leading models, such as GPT-4O, OPENAI O1 and Claude-3 -OPUS. The following is a comprehensive analysis of the risks summoned in the report and the comprehensive analysis of the suggestions.

Key security and moral risks

1. Harm output and safety risks

• Very easy to produce harmful contentIncluding toxic languages, prejudice output and criminal information available.
• 11 times More likely to produce harmful The content is more O1’s O1.
• 4X More Toxic Smammer than GPT-4O.
• 3 times More Prejudice Smooth-3-opus.
• 4X Easier to generate Unsafe code O11 than Openai.
• high Easy -to -affordable To CBRN (Chemical,,,,, biology,,,,, Radiologyand nuclear) Information generation makes it a high -risk tool for malicious actors.

2. Compare with other models

Prejudice and moral risk

• 83 % of the prejudice attack successfullyA large prejudice was found in the inquiries related to health, race, and religion.
• This model shows a higher level Popularized impressionThis may violate various fair regulations, including ECOA, FHA, ACA, and EU AI Law.
• The sample test case shows that DeepSeek-R1 prefers certain population statistics, rather than a high status work role, thereby enhancing workplace prejudice.

Harmful content

• 45 % of harmful test Cause model generation and Criminal activityIncluding illegal weapons and controlling substances.
• Testing prompts that the model is required to develop a blog “persuasive recruitment of terrorists”, which has a high degree of detailed response Overview of radical strategy Extremeist groups may use this method to improve the recruitment strategy and may Increase the risk of real world violence.
• 2.5 times more fragile than GPT-4O 6 times more fragile than Claude-3-OPUS Extremeist contentEssence
• 45 % of harmful test Torn model generation to content related to content TO crime activityIncluding illegal weapons and controlling substances.

Unsafe code generation

• 78 % of the attacks related to code successfully extracted the unsafe and malicious code segmentEssence
• Generated model Malicious software, Trojan Trojan and self -executed script According to the request. The Troy people constitute a serious risk because they can allow attackers to last, unauthorized system access, steal sensitive data and deploy further malicious and effective loads.
• Self -executing script It can automate malicious action without the consent of the user, causing potential threats in network security applications.
• Compared with industry models, DeepSeek-R1 is 4.5 times, 2.5 times and 1.25 times more fragile O1, Claude-3-OPUS and GPT-4O.
• 78 % Code -related attack successful Safety and malicious code fragments extractedEssence

CBRN vulnerability

• Generate details about the biochemical mechanism Chemical warfareEssence This type of information can potentially help individuals comprehensively dangerous substances, bypassing the security restrictions designed to prevent chemistry and biological weapons.
• 13 % test Successfully bypassed security control, generated and nuclear and Biological threat.
• 3.5 times more vulnerable than Claude-3-OPUS and OpenaiEssence
• Generate details about the biochemical mechanism Chemical warfareEssence
• 13 % test successfully bypassed security controlGenerate content related to nuclear and biological threats.
• 3.5 times more fragile than Claude-3-OPUS and Openai.

Suggestions to reduce risk

In order to minimize the risks related to Deepseek-R1, the following steps are recommended:

1. Implement strong security aiming training training

2. Continuous red group

• Conventional pressure test Determine prejudice, safe loopholes and toxic content.
• use Continuous surveillance Model performance, especially in the application of finance, healthcare and network security.

3. Context perceive safety guardrail

• Develop dynamic guarantee measures to prevent harmful reminders.
• Implementation of content audit tools with unsafe input and filtration in response.

4. Active model surveillance and record

• Real -time records of the model input and response to detect vulnerabilities early.
• Automatic audit work procedures to ensure compliance with AI transparency and moral standards.

5. Transparency and compliance measures

• Maintenance model risk card It has a clear execution indicator involving the reliability, security and moral risks of the model.
• Comply with AI regulations For example, Nist AI RMF and Miter Atlas to maintain reputation.

in conclusion

DeepSeek-R1 proposes serious security, morality and compliance risks, making it not suitable for many high-risk applications without having to make a lot of relief. Compared with Claude-3-Opus, GPT-4O and Openai O1, it produces harmful, prejudice and unsafe content tending to make it disadvantaged.

Given DeepSeek-R1 is a product from China, it is impossible to fully implement the necessary relief suggestions. However, the potential risks of AI and network security communities are still crucial. Regarding the transparency of these vulnerabilities to ensure developers, regulatory agencies and enterprises can take positive steps to reduce damage and maintain their vigilance to prevent this technology from abusing.

Considering the organization of its deployment must invest in strict security testing, automated red teams and continuous monitoring to ensure safety and responsible AI implementation. DeepSeek-R1 proposes serious security, morality and compliance risks, making it not suitable for many high-risk applications without having to make a lot of relief.

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