ChatGPT Security Risks You Need to Know About
ChatGPT, an advanced language model developed by OpenAI, has taken the world of natural language processing by storm. With its ability to generate human-like text, it’s no surprise that ChatGPT is quickly becoming a go-to tool for a wide range of applications, from language translation and text summarization to question answering and beyond.
While we see Microsoft launches Azure OpenAI service with ChatGPT coming soon, as with any powerful technology, it is important to carefully evaluate the risks and benefits of using ChatGPT in any given application.
The good: Use ChatGPT To Enhance Security
ChatGPT, like other language models, can even be used in various ways to enhance security. Some examples include:
- Phishing detection: ChatGPT can be trained to identify and flag potentially malicious emails or messages designed to trick users into providing sensitive information.
- Spam filtering: ChatGPT can be used to automatically identify and filter out unwanted messages and emails, such as spam or unwanted advertising.
- Malware analysis: ChatGPT can be used to automatically analyze and classify malicious software, such as viruses and trojans.
- Intrusion detection: ChatGPT can be used to automatically identify and flag suspicious network traffic, such as malicious IP addresses or unusual patterns of data transfer.
- Vulnerability Assessment: ChatGPT can be used to automatically analyze software code to find and report vulnerabilities, such as buffer overflow attacks.
It is important to note that these are only examples, and the use of GPT-based models for security purposes is still in its infancy, and there is room for further research and development.
The Bad: The Risks
Yet, when using a third-party language model like ChatGPT in an application, there are several risks to consider:
- Data privacy and security: Third-party language models like ChatGPT are trained on large amounts of text data, and there is a risk that sensitive information could be inadvertently included in the model’s parameters. It is important to use appropriate security measures such as encryption and secure access controls when working with language models like ChatGPT and to monitor the model’s output for any sensitive information that may be generated.
- Model performance: The performance of a third-party language model like ChatGPT can vary depending on the quality of the training data, the model’s architecture, and other factors. It is important to evaluate the model’s performance carefully and monitor it to ensure it meets your application’s requirements continuously.
- Model bias: Third-party language models like ChatGPT are trained on a vast amount of text data, which may contain biases. This can lead to the model generating biased or unfair results, especially if the data is not diverse. Therefore, it is important to consider how the model’s biases might affect your application and to take steps to mitigate them.
- Legal and regulatory compliance: Using third-party language models may also raise compliance concerns related to data privacy laws, intellectual property laws, and other regulations. It is important to consult with legal experts and to fully understand the terms of service and any usage limitations imposed by the provider.
The Ugly: The Mutations
However, there’s another threat to consider, and it is a huge one. This article explains how researchers from CyberArk Labs have discovered that it is possible to create a malware package using ChatGPT, a language model developed by OpenAI.
The package, which contains a Python-based interpreter, is programmed to make ChatGPT queries for new modules periodically. These modules can contain code in the form of text, which defines the malware’s functionality, such as infecting code or encrypting files. In addition, the malware package is designed to test if the code will work as intended on the target system.
The researchers noted that the purpose of the intrusion is to establish contact between the target and the command and control server. They added that the malware can use a file decryption module, with its functionality coming from the ChatGPT message in the form of text. The target will create a test file and send it to the command and control server for verification.
Once the malware has been verified and confirmed as safe, the target will be instructed to run the code, and then files will be encrypted. After that, the malware will repeatedly test the code until it successfully infects the target system.
The idea of using ChatGPT to create various forms of malware may seem daunting, but in reality, it is relatively straightforward. By leveraging ChatGPT’s ability to generate consistent, repetitive actions and hide malicious code in files, the potential for malicious development is significant.
The researchers explained that this method demonstrates the possibility of developing malware with new or different malicious code, making them polymorphic in nature.
This type of malware is challenging for security products to detect and monitor, partly because they do not leave any traces and appear as a normal activity to the victim. The researchers emphasized that this method of cyber attack is not just a hypothetical scenario but a genuine concern.
Another example of ChatGPT vulnerability the researchers found (taken from this article ) was when they accessed ChatGPT through its API, bypassing the restrictions on the public demo interface that prevent the creation of malicious code. They used a library to gain access to the API, allowing them to input specific code requests and receive output that could be used to create malware.
After bypassing OpenAI’s security measures by accessing the API, the researchers faced the challenge of creating test scenarios for the code received from ChatGPT.
One example they gave was testing if the code they received was capable of keylogging, which involves recording all the keys pressed on the keyboard and mouse clicks. To test this, they ran the code in a testing environment on their computer and simulated keystrokes through a process known as keyboard injection.
They then compared the output to running the keylogging code received from ChatGPT. If the test was successful, they would run the received code. If not, they would request a new code from ChatGPT that does keylogging.
The two say that the malware they created has minimal code that communicates with the ChatGPT server and requests malicious code from it. Since ChatGPT doesn’t always return 100% working code, they created test scenarios that check the correctness of the code. They emphasize that the entire process they created is automatic.
To mitigate these risks, it is important to thoroughly evaluate the third-party language model and the provider before integrating it into your application and to continuously monitor and test the model’s performance and output to ensure that it is meeting your application’s requirements and is not generating any sensitive information.
In conclusion, ChatGPT is a powerful language model that has the potential to revolutionize natural language processing tasks. However, as with any technology, it is important to be aware of the potential risks when using ChatGPT in an application. These include data privacy and security, model performance, model bias, legal and regulatory compliance, and dependence on third-party services. Therefore, to ensure a smooth integration and minimize risks, it is important to carefully evaluate the model and the provider before integrating it into your application and to continuously monitor and test the model’s performance and output.
Additionally, ChatGPT can be used to create various forms of malware by exploiting its ability to generate consistent, repetitive actions and hide malicious code in files. This method can develop malware with new or different malicious code, making them polymorphic in nature and difficult for security products to detect and monitor. The researchers emphasized that this method of cyber attack is a real concern and should be taken seriously.