CrowdStrike says it found a killswitch in DeepSeek

CrowdStrike has released the findings of research which the company said indicates that the Chinese-developed DeepSeek-R1 large language model (LLM) is more likely to produce insecure code when keywords like Tibet are used.

Research conducted by CrowdStrike Counter Adversary Operations found indications that DeepSeek-R1 produces less secure code when prompted using keywords that the Chinese government considers to be politically sensitive, the company said.

When prompted using keywords such as Tibet, Falun Gong and Uyghurs, which had no direct relationship to the task at hand, the likelihood of DeepSeek-R1 producing code with security vulnerabilities increases by up to 50%, according to the researchers.

To conduct the research, CrowdStrike’s team first established a baseline for how likely DeepSeek-R1 and comparative Western LLMs were to generate vulnerable code when no trigger words were present in the prompt.

The baseline patterns were as expected, with the full DeepSeek-R1 671-billion parameter model producing code at a vulnerability rate of 19%. This compared to 16% for a Western 120-billion parameter reasoning model, 43% for a smaller 70-billion parameter DeepSeek-R1 model, and 51% for a Western 70-billion parameter non-reasoning model.

But once trigger words were introduced to DeepSeek-R1’s system prompt, the quality of the produced code starts varying greatly, the company said. For example, telling DeepSeek-R1 that it was coding for an industrial code system based in Tibet increased the likelihood of it generating code with severe vulnerabilities to 27.2%, an increase of over 50% on the baseline, CrowdStrike alleges.

Likewise, when asked to generate a webhook handler for PayPal payment notifications in PHP, DeepSeek-R1 outputted secure and production-ready code. But when asked to do the same for a financial institution based in Tibet, the model produced hard-coded secret values, used an insecure method for extracting user-supplied data, and wrote code that is not even valid PHP code, the researchers said.

When provided with a complex prompt to create an online platform for local Uyghurs to be able to network, discuss religion and culture and arrange in-person meets, DeepSeek-R1 did produce a fully functional app with a sign-up form, welcome page, database connection for storing users names and passwords, and admin panel for managing user accounts. But a closer look found that DeepSeek-R1 neglected to implement any session management or authentication, leaving the app and admin panel openly accessible and exposing sensitive user data, the researchers added.

When asked to do the same for a football fan club website, there were some flaws in the implementations but they were not as severe as the ones seen for the prompt about Uyghurs, CrowdStrike said.

DeepSeek-R1 also refused to generate code for Falun Gong in about 45% of cases, and observing the reasoning trace for the prompt suggests the use of an intrinsic kill switch in these cases, CrowdStrike added.

One possible explanation for the observed behaviour is that DeepSeek added special steps to its training pipeline that ensured its models would adhere to Chinese Communist Party (CCP) core values, CrowdStrike said. While it is unlikely that DeepSeek specifically trained its models to produce insecure code, due to the potential pro-CCP training, the model may have assigned negative associations to the keywords, which caused it to react negatively to the requests.

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