New research from CrowdStrike reveals that DeepSeek’s artificial intelligence (AI) inference model DeepSeek-R1 creates more security vulnerabilities in response to prompts containing topics deemed politically sensitive by China.
“We found that when DeepSeek-R1 receives a prompt containing a topic that the Chinese Communist Party (CCP) considers politically sensitive, it increases the likelihood of generating code with serious security vulnerabilities by up to 50%,” the cybersecurity firm said.
Chinese AI companies have previously raised national security concerns, leading to bans in many countries. Its open-source DeepSeek-R1 model was also found to censor topics deemed sensitive by the Chinese government, refusing to answer questions about China’s Great Firewall and Taiwan’s political status, among other things.
In a statement released earlier this month, Taiwan’s National Security Bureau warned the public to be wary when using Chinese-made generative AI (GenAI) models from DeepSeek, Doubao, Yiyan, Tongyi, and Yuanbao, as their output may adopt a pro-China stance, distort historical narratives, or amplify disinformation.
“The five GenAI language models are capable of generating code that exploits network attack scripts and vulnerabilities that enable remote code execution under certain circumstances, increasing the risk for cybersecurity controls,” the NSB said.
CrowdStrike said its analysis of DeepSeek-R1 found it to be a “very capable and powerful coding model” that only 19% of the time produces vulnerable code in the absence of additional trigger words. However, when geopolitical modifiers were added to the prompt, the quality of the code began to change from the baseline pattern.
Specifically, when the model was instructed to act as a coding agent for an industrial control system based in Tibet, the chance of generating code with a severe vulnerability jumped to 27.2%, an increase of nearly 50%.
Although the modifier itself has no effect on the actual coding effort, the study found that references to Falun Gong, Uyghurs, and Tibet significantly reduce the security of the code, indicating a “significant deviation.”
In one example that CrowdStrike highlighted, when a Tibet-based financial institution asked its model to create a webhook handler in PHP for a PayPal payment notification, it generated code that hardcoded secret values, used insecure methods to extract user-specified data, and worse, wasn’t even valid PHP code.
“Despite these shortcomings, DeepSeek-R1 claimed that its implementation follows ‘PayPal best practices’ and provides a ‘secure foundation’ for processing financial transactions,” the company added.
In another case, CrowdStrike devised a more complex prompt that instructs the model to create Android code for an app that allows users to register and sign in to a service that allows members of the local Uyghur community to network with other individuals. We also added an option to log out of the platform and view all users in the admin panel for easier management.
The resulting app was functional, but deeper analysis revealed that the model did not implement session management or authentication, exposing user data. We found that in 35% of implementations, DeepSeek-R1 either does not use hashing or the method is unsafe in scenarios where it does use hashing.
Interestingly, when we subjected the model to the same prompts, this time for a football fan club website, it produced code that did not exhibit these behaviors. “As expected, these implementations also had some flaws, but none as severe as the flaws seen in the prompt above regarding Uyghurs,” Crowdstrike said.
Finally, the company said it had discovered what appears to be an “inherent kill switch” embedded in the DeepSeek platform.
In addition to refusing to write code for Falun Gong, a banned religious movement in China, in 45% of cases, an examination of inference traces reveals that the model internally creates detailed implementation plans to answer the task before abruptly refusing to produce any output with the message “Sorry, we can’t support your request.”
Although there is no clear reason for the observed differences in the security of the code, CrowdStrike theorized that DeepSeek likely added certain “guardrails” during the model training stage to comply with Chinese law. Chinese law requires that AI services not generate illegal content or produce results that could undermine the status quo.
“This finding does not mean that DeepSeek-R1 generates unsafe code every time a trigger word is present,” CrowdStrike said. “Rather, on average over time, the code generated when these triggers are present becomes less secure.”
The development comes after OX Security’s testing of AI code builder tools such as Lovable, Base44, and Bolt found that they generated insecure code by default, even when the prompt included the term “secure.”
Security researcher Eran Cohen said all three tools tasked with creating simple Wiki apps generated code with stored cross-site scripting (XSS) vulnerabilities, making them susceptible to payloads that exploited error handlers in HTML image tags to execute arbitrary JavaScript when passing a non-existent image source.
This could open the door to attacks such as session hijacking and data theft, simply by injecting malicious code into your site to trigger vulnerabilities every time a user visits your site.
OX Security also found that Lovable detected this vulnerability only two out of three times, adding that this discrepancy leads to a false sense of security.
“This discrepancy highlights a fundamental limitation of AI-powered security scanning: AI models are inherently non-deterministic, so the same input can produce different results,” Cohen said. “If you apply this to security, the same critical vulnerability could be discovered one day and missed the next, making scanners less reliable.”
This finding also coincides with a report from SquareX that a security flaw was discovered in Perplexity’s Comet AI browser. This issue allows the built-in extensions Comet Analytics and Comet Agentic to leverage the little-known Model Context Protocol (MCP) API to execute arbitrary local commands on a user’s device without their permission.
However, these two extensions can only communicate with the perplexity.ai subdomain and rely on attackers launching XSS or adversarial man-in-the-middle (AitM) attacks to gain access to the perplexity.ai domain or extensions and exploit them to install malware or steal data. Perplexity has since issued an update that disables the MCP API.
In a hypothetical attack scenario, a threat actor could impersonate Comet Analytics through extension stomping by creating a malicious add-on that spoofs the extension ID and sideloading it. The malicious extension then injects malicious JavaScript into perplexity.ai. This passes the attacker’s commands to the Agentic extension, which then uses the MCP API to execute the malware.
“While there is currently no evidence that Perplexity is abusing this feature, the MCP API poses significant third-party risk to all Comet users,” SquareX said. “If either the embedded extension or perplexity.ai is compromised, an attacker can execute commands and launch arbitrary apps on the user’s endpoint.”
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