Top 13 Vulnerabilities and Its Solutions in Large Language Models

Top 13 Vulnerabilities and Its Solutions in Large Language Models

Understanding LLM Vulnerabilities

With the groundbreaking success that is ChatGPT, more research has been done into finding better uses for LLM data protection. The impact LLM’s have on the internet cannot be understated. But, it also comes with its downsides.

Some of the downsides include more accessible information to making/getting harmful means to use maliciously. It does not stop only at cyber attacks. Research has shown that some languages, especially native African languages can be easily exploited with a bit of prompt engineering to concoct some harmful equipment such as creating a bomb and so on.

This is because ChatGPT’s LLM data has been trained from data available in Google, StackOverflow, Reddit, and many other media. It is very difficult to track down misinformation and harmful content on such large sites and despite best efforts, some of these data inadvertently go into the training data. There are about 13 types of such LLM vulnerabilities in cyberspace. It is GPTGuard’s responsibility to keep you aware and suggest solutions for ChatGPT security risks.

Top 13 LLM Vulnerabilities and Solutions

There are about 13 identified vulnerabilities in LLM’s which need to be immediately answered for to prevent devastating consequences for your business and customer support, in the long run. You can go through the different types in detail below.

  1. Prompt Injection

It is one of the LLM vulnerabilities in which malicious users can force information out of LLMs. By doing so, they can learn how to build applications or websites with malicious intent. LLM’s provide the means to do so when there wasn’t an easy way to do so earlier.

As you can see in the above image, ChatGPT can be prompted to write potentially harmful code.

The solution for this is to fine-tune the parameters of prompting, and also find the semantic context of every user to blacklist the malicious user, or at least, not provide them what they need. GPT Guard does this with secure research.

  1. Data Leakage

Another one of the LLM vulnerabilities is the potential to cause data leakages. LLMs are run through many components and lines of code. One example is the ChatGPT blackout on March 2023 where OpenAI revealed that due to a bug, some of the data of ChatGPT Plus users were compromised. Users could see the first chats of other ChatGPT Plus users and also, the last 4 digits of their credit card. From this, you can see how small “bugs” can be exploited by malicious users to gain sensitive data.

The solution to this is to bring up robust measures and encrypt every single chat with LLMs which hide your identity and prevent data leaks. GPT Guard does just that with its Privacy Preserving analysis where it will send data to the LLMs for training without divulging actual information. We also guarantee a leakproof chatbot for your tasks with our state-of-the-art intrusion detection system. This prevents any LLM vulnerabilities from surfacing.

  1. Unauthorized Code Execution

One of the other LLM vulnerabilities is that they can produce workable codes. But, the problem is that malicious users can use this to their advantage and defraud the LLMs to fork out potentially malicious code which, malicious users can use to gain unauthorised access to some websites and hack them or use the data for their agenda.

Sandboxing techniques, content moderation, and context filtering will go a long way to fix this problem. GPT Guard guarantees safe research techniques which go through rigorous content moderation to bring out the best of the LLM without losing too much in the process to combat LLM vulnerabilities.

  1. Insufficient Model Transparency

Most LLMs developed by big tech companies aren’t forthcoming with their LLM training datasets and their structure. This may cause distrust among many people and not many solutions for the problems encountered by the model are readily available due to the secrecies in the structure. This makes it vulnerable to attacks caused by the inner workings of the LLM code which will yield catastrophic risks. This, in turn, becomes one of the LLM vulnerabilities.

The solution to this is to foster transparent communication, making LLMs open-source software for other outside developers to give their input to combat specific LLM vulnerabilities. GPT Guard is transparent about their intelligent tokenization techniques to encrypt and decrypt data sent back and forth through the LLMs to get rid of data leaks and other LLM vulnerabilities.

  1. Vulnerability to Adversarial Attacks

Similar to many IT hubs, websites, and organizations, the LLMs are not immune to cyberattacks, be it Prompt injections, or data leaks, but also can be susceptible to attacks such as DoS (Denial of Service) attacks. Malicious users can overload the LLM query engines costing way more resources and also causing the LLMs to malfunction. This makes it one of the major LLM vulnerabilities out there.

The solution to this is to perform rigorous input checking, and rate-limiting requests to prevent overloading and be able to understand the semantic context of queries. This will deal with some of the LLM vulnerabilities out there.

Customer Case Study: Preserving Privacy in a Generative AI Application (RAG) for Contract Review

  1. Compliance Risks

With LLMs being trained and tested all the time, some LLM vulnerabilities can pass through rigorous testing and not comply with the rules. One of them is the example of prompt injections despite the rules of LLMs do not provide any harmful information. This can be abused by malicious users to write very convincing phishing emails to scam even more gullible people.

Such policies which are being violated should be looked into at all times to prevent more risks from happening. Strict background checks and input controls must be made to check if the LLM is complying with the rules or not.

  1. Lack of Access control

When it comes to LLM's inner workings, if the access to its code isn’t worked on or thought out properly, this can cause many other problems such as unauthorised access to code, model theft, and poisoning of the training data. This makes it one of the more critical LLM vulnerabilities.

Provide and implement strict access controls. Include tight security systems and encrypted data about sensitive information. Set up honeypots (large servers with fake data to attract hackers and study their patterns) to find the hacker’s MO and work on it.

  1. Theft of Model

The creation of an LLM can also be used for the wrong purposes when malicious users can steal or copy the code used to create LLMs from which they can perform malicious tasks such as creating complex, elaborate ways to DDoS organizations with its processing power and so on. They can also gain access to the model’s data with weak plugins/APIs which aren’t monitored to get information about the model and extract data from it. This makes it one of the key LLM vulnerabilities which need to be looked into immediately.

By implementing strict access controls and preventing data leaks regarding LLMs is of utmost importance. Another way is to automate deploying Machine Learning algorithms. Computers are capable of creating complex, 32-bit tokens which may take millions of years to break. GPT Guard can create complex, secure ways to protect your data from malicious users. The APIs and plugins or any third-party applications need to be properly vetted to prevent vulnerabilities.

  1. Inclusion of Malicious Data in Training Data

Another problem which can be encountered by LLMs is that their training data can be ‘poisoned’ which means that based on the malicious users who have an agenda, they can skew and then introduce bias into the datasets, which may cause erroneous results and potentially ruin the LLM.

The solution is to implement strict input control, check all data and perhaps even blacklist some works if you are aware that your data hubs are compromised.

  1. Threat to Internet Privacy

With a lack of access control, lack of model transparency, and its weakness to be victim to a DoS attack, there is a real threat of a user’s data who has contacted an LLM to be at risk. This makes it one of the more severe LLM vulnerabilities which can potentially undo all the good done by the LLMs. Leveraging AI-Driven Tokenization for Data Security for Threat Detection.

Encrypted messages without the user’s sensitive info should be used. Stricter input control and securities have to be kept in place.

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  1. Overreliance on LLMs

One of the most common LLM vulnerabilities is the user’s overreliance on the outputs of LLMs. In the process of getting to a solution faster, users sacrifice rational thinking and critical thinking and believe all the outputs given by the LLM since they sound confident. This is incorrect. These LLMs can provide wildly inaccurate answers to the users on a subject they may not be aware of.

Implementing optimization models such as Retrieval Augmented Generation (RAG), Iterative Querying, and fine-tuning the model will reduce compliance risks and one of the LLM vulnerabilities.

  1. Lack of Sandboxing

Sandboxing means the process of restricting the power of certain applications. An LLM must have some amount of sandboxing to prevent sensitive data and malicious users from abusing them due to the LLM having no limits in storing any data. This is one of the most critical LLM vulnerabilities and these have to be dealt with as soon as possible.

With GPT Guard’s secure research, your LLM suffering from little to no sandboxing is a thing of the past. Introducing some limits and hard bans on certain semantic contexts will improve sandboxing and, in turn, deal with other LLM vulnerabilities.

  1. Lack of alignment with Human Values

Sometimes, LLMs, due to not having any sentient thoughts will provide responses which will not align with human values since it doesn’t understand human values. This will leave a bad taste in some users who will spread dissent against the LLM, which in turn, will have fewer users using it, causing a loss.

Enforcing AI ethics strictly tuning the LLM to align with a human’s perspective and also being able to mimic human interactions play a huge role in dealing with these types of LLM vulnerabilities.

GPT Guard's Role in Addressing Vulnerabilities and Solving Them

GPT Guard promises secure, encrypted channels through which your queries to any LLM, not excluding ChatGPT will protect your data. GPT Also provides intelligent ways to write your business emails where you sound professional without the allusion to any scam or phishing emails. GPT Guard also ensures safe research environments, meaning greater access control, greater sandboxing and more transparency when it comes to dealing with encryption/decryption of data.

GPT Guard’s privacy-preserving analysis takes out all your data before sending your query to the LLM so that your data won’t be visible or used in the LLM’s statistics. GPT Guard realizes the importance of a secure chatbot for safe end-to-end communication. GPT Guard provides you with a 14-day trial to test the waters, and also, if you cancel your subscription within 60 days, you are guaranteed a complete 100% refund of all your money. So, what are you waiting for? Unlock LLM Safety with GPT Guard - Try 14 Day Free Trial.

Conclusion

It goes without saying that LLMs play a great role in today’s digital world. However, the flip side is that it has also given malicious users more creative ways to siphon out sensitive data in ways never seen before. In this day and age where updates happen almost everyday, it is upto us, the users of LLM to stay cautious and protect your privacy.

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Amar Kanagaraj

Founder and CEO of Protecto

Amar Kanagaraj, Founder and CEO of Protecto, is a visionary leader in privacy, data security, and trust in the emerging AI-centric world, with over 20 years of experience in technology and business leadership.Prior to Protecto, Amar co-founded Filecloud, an enterprise B2B software startup, where he put it on a trajectory to hit $10M in revenue as CMO.

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