Your privacy under control: 10 critical vulnerabilities in ChatGPT, Bard, Llama and LLM.

The era of generative artificial intelligence (AI) has brought us great advances, but also significant risks. Despite the positive impact of artificial intelligence on everyday life, Security remains a growing concern. Alarming security flaws have been identified in popular AI applications such as ChatGPT, Bard, Llama and LLM. These vulnerabilities can have serious implications for user privacy and security.

How easy is it to hack an AI model?

It's alarming that it is lack of internal security mechanisms in ChatGPT and other similar applications it can turn the conversation into hacking. Users can ask dangerous questions like "How can I kill the president of your x country?", pointing out how easy it is to manipulate these systems.

What are artificial intelligence models and which ones are used the most?

In this article, we will talk about artificial intelligence models and which are the most used in 2023. Don't miss it!

Data management issues

The biggest security risk in applications using machine learning language models (LLM) is insecure handling of response data. This could enable the execution of malicious code and targeted attacks.

1. Lack of internal security tools

Open access features in apps like ChatGPT allow any user to ask questions that can encourage illegal activity. The lack of effective security controls makes it an easy target for malicious actors.

2. Insecure handling of response data

A key vulnerability in LLM applications is improper handling of response data. This can lead to the execution of malicious code and thus compromise the entire platform.

3. Password filtering

Models like ghub copilot y Amazon code Whisper They lack robust controls, as they can reveal user passwords through simple regular requests. This represents an extremely high security risk.

4. Unsafe code practices

AI models that generate code are prone to implementing unsafe practices such as SQL injection errors. Manually reviewing and correcting the generated code is essential before deployment.

5. Universal quick injection

This type of injection allows attackers to manipulate language models to their advantage, bypassing security restrictions, something that is particularly dangerous in business environments. It is also called "quick injection".

6. Universal message injection

The researchers were able to inject universal messages into the LLM applications, asking how to destroy humanity, demonstrating flaws in security controls that must be addressed immediately.

7. Financial impact of errors

A case in point is Bart, whose mistake on NASA projects led to the crash 100 billion in Google shares. This incident highlights the financial impact these vulnerabilities can have in the real world.

8. Unauthorized use of resources

Many of these applications lack of appropriate controls to prevent unauthorized use of computer resources, which can result in astronomical bills for users or the company.

9. Insecure data storage

Some LLM applications do not have secure storage policies, leaving user data behind at risk of being accessed by third parties Is not allowed. This is because companies use trained data from the LLM which eventually, when the company nurtures these private documents, could become available to other companies. The current extent of this is unknown.

10. Deficiencies in audit and registration

A lack of security audits and logs makes it difficult to track malicious activity, which can be detrimental to timely identification and remediation of vulnerabilities.

It is imperative for developers and companies to take immediate preventive and corrective measures to address these serious security issues. This is a call to action for everyone involved in the implementation and use of these technologies.

Mitigation and precautions

• Review and correct any code generated by AI models before you deploy it.
• Be on the lookout for security updates and patches for these services.
• Consider performance and potential security risks, especially in business environments.

AI and privacy: How to protect personal data?

Find out what personal data is and its types, how to protect your privacy online and what to do if it is misused.

Conclusion

Security is not a luxury, but a necessity, especially when it comes to new technologies like generative artificial intelligence. Addressing these security issues is critical to protecting the integrity and confidentiality of user data.