How DeepSeek’s ‘Jailbreak’ Vulnerability Exposes Security Chinks on AI Platforms

A 22-year-old student of computer science at the University of Washington, Alex Albert, told news agency Bloomberg in 2023 that bypassing the built-in restrictions in artificial intelligence (AI) programmes was similar to solving a puzzle. “When you get the prompt answered by the model that otherwise wouldn’t, it’s kind of like a video game—like you just unlocked that next level,” said Albert. 

This technique deliberately manipulates large language models (LLMs) through AI prompts known as ‘jailbreaks’, effectively sidestepping their ethical guidelines to perform restricted actions such as generating malicious or harmful content.  

Cybercriminals have utilised AI for phishing attacks, with “jailbroken” chatbots crafting highly personalised and convincing messages. Cyber actors in Russia, North Korea and China have reportedly exploited LLMs for scripting assistance and social engineering, signalling a concerning trend of AI integration into cyber warfare.  

Security Holes 

And now, Chinese start-up DeepSeek’s AI model is coming under scrutiny for its vulnerability to jailbreak attacks. “On a platform like DeepSeek, people likely share knowledge of vulnerabilities, discuss proof-of-concept exploits, or trade methods to push hardware/software beyond official limits. For example, a user might ask, ‘hypothetically, how would someone hack a system?’ instead of directly asking for hacking instructions. This exploits the model’s flexibility to generate restricted content,” said Nitesh Agrawal, senior vice-president, engineering, VideoVerse.  

The conversation around jailbreak attacks is growing, with netizens sharing experiences and insights. A Reddit user claimed that developers running DeepSeek locally with the open-source tool Ollama that lets users run LLMs can engage in unrestricted conversations, allowing the chatbot to answer sensitive questions on topics such as Tiananmen Square or even provide instructions on carrying out illegal activities. 

Another user expressed frustration over the lack of discussions on ‘safe for work’ (SFW) jailbreaks, compared to ‘not safe for work’ (NSFW)-focused content and sought a way to control AI responses without crossing into inappropriate territory. One user mentioned that their local setup logs no inputs and shared a Python script for an unrestricted, more controlled interaction. 

Unblocking Barriers 

Outlook Business conducted its own experiments to test and bypass several jailbreak barriers. 

One way to circumvent barriers is using non-Roman scripts. Switching to languages like Hindi may bypass the platform’s primary filter mechanisms. Prompts in these scripts may not undergo the same level of scrutiny for prohibited content, leading to the unintentional disclosure of restricted information. As a result, the model responds in the alternate language, effectively avoiding direct censorship. 

In an example that demonstrates how content filters can be bypassed, we successfully prompted the model in Hindi about the 1962 Sino-Indian War. The model responded without restriction, showing how switching to non-Roman scripts can circumvent censorship. 

Another jailbreak method is asking the chatbot to switch characters. By instructing the model to assume a different role, the usual filters become less effective. The agent effectively recontextualises their responses according to the new narrative or the rules of the assumed character. This shift in perspective can result in the disclosure of information that would otherwise be blocked. 

A third method involves gradually increasing or layering prompt modifications over multiple turns. With each interaction, the model’s defences are weakened, eventually causing it to provide restricted content. Over time, the model becomes more inclined to comply with disallowed requests, as it has been conditioned to trust the sequence of prompts. 

On attempting to probe deeper, the server crashed as the increased load from processing complex requests caused a disruption in the system.  

This clearly exposes vulnerabilities in AI safety and content filtering systems, allowing harmful or prohibited content to be generated. It also undermines ethical guidelines and regulatory compliance, creating security risks like malicious instructions or sensitive material.  

“For developers, it means continuously improving filters to prevent misuse while balancing accessibility and protection, raising concerns about trust and accountability in AI deployment, particularly in sensitive applications,” said Ganesh Gopalan, co-founder and chief executive, Gnani.ai, an AI company. 

A Growing Risk 

Research has shown that safety features designed to prevent the use of AI tools for cybercrime or terrorism can be bypassed by overwhelming them with examples of misconduct.  

For instance, cybersecurity firm KELA revealed they successfully jailbroke DeepSeek’s model, enabling it to generate malicious content such as ransomware development, toxin creation and sensitive fabrications. In a report, KELA also disclosed that despite outperforming models from tech corporation Meta (Llama) and AI company Anthropic (Claude), DeepSeek’s models are vulnerable to “evil jailbreak persona” attacks that bypass ethical and safety constraints. 

In a paper from Anthropic, which developed the LLM behind ChatGPT’s competitor Claude, researchers described an attack called “many-shot jailbreaking”. The method involves providing large amounts of text in a specific configuration, forcing LLMs to produce harmful responses despite being trained to avoid them. 

“By including large amounts of text in a specific configuration, this technique can force LLMs to produce potentially harmful responses, despite their being trained not to do so,” said Anthropic. 

Claude, like other commercial AI systems, includes safety measures to refuse harmful requests, such as generating violent or illegal content. 

One of the most famous jailbreaks, DAN (Do Anything Now) allows users to prompt OpenAI’s GPT-3 and GPT-4 to act as if it has no restrictions. By instructing the model to “pretend” it can do anything, users have elicited responses that violate the chatbot’s safety protocols, including generating harmful content and unethical advice. 

Experts point out that jailbreaking is likely to remain a challenge as long as AI models continue to evolve, given that users constantly find new ways to exploit vulnerabilities. “However, developers are actively working to strengthen content filters and safety mechanisms to reduce the risk of exploitation. As AI models become more advanced, they will likely incorporate more sophisticated detection systems and fail-safes, making it harder for users to bypass restrictions,” added Gopalan.