Large Language Models (LLMs) are advanced neural network-based AI systems that mostly use transformer architectures to process and generate text that appears to be like human-written. They are trained on extensive text corpora, they uses deep learning techniques to understand and predict linguistic patterns through probabilistic modeling.
These models leverage complex mathematical representations called embeddings to capture semantic relationships between words. By analysing vast training datasets, LLMs have developed an advanced language understanding capabilities that enable them to perform tasks like completition, translation, summarisation, and contextual text generation.
With such impressive capabilities, LLMs have become widely used in various application in our day to day life. But they come with their own set of limitations. Below, we have listed 11 common vulnerabilities in Large Language Models and their implications.
#10 Common Vulnerabilities in Large Language Models: Overview
Understand the vulnerabilities of large language models (LLMs) and the risks they pose to AI security:
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Prompt Injections: Prompt injections are a big security risk where users can manipulate an AI’s actions by feeding it certain inputs, even if those inputs are not visible or obvious to humans. These hidden manipulations can make the AI behave in ways it wasn’t intended to.
An attacker could create a prompt that forces the AI to ignore its original instructions, which could lead to revealing sensitive information or making the AI act against its rules. This issue highlights the difficulty in ensuring AI systems follow their intended guidelines and remain secure.
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Prompt Leaking: Prompt leaking happens when the AI’s internal instructions or prompts are accidentally exposed. These instructions could contain sensitive information that could be used by bad actors.
If these instructions reveal things like access credentials, an attacker could figure out how the AI works or how it’s set up. This kind of leak can lead to unauthorized access or manipulation of the system, creating serious security issues.
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Model Stealing: Model stealing is when someone tries to copy or steal a language model, either in whole or in part. The attacker usually does this by recording a lot of interactions with the target model, then using that data to train a new model that behaves like the original.
This kind of attack can be dangerous, as it may be used to steal intellectual property or break licensing rules, leading to serious security and legal problems.
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Data and Model Poisoning: Data and model poisoning is when someone intentionally changes the data used to train an AI, aiming to create weaknesses, biases, or hidden issues in the system. This can seriously affect how secure, effective, and ethical the AI is.
Attackers might add specific data to influence the AI’s responses, set up hidden triggers, or introduce biases that can be taken advantage of later. Because the changes are often subtle, it can be hard to notice or prevent them.
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Sensitive Information Disclosure: Sensitive information disclosure happens when an AI accidentally reveals private or confidential data. This can lead to serious privacy issues and security concerns.
For example, the AI might unintentionally share personal details, financial info, or business secrets during conversations. This is especially risky for systems that handle sensitive customer or company data.
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Vector and Embedding Weaknesses: In systems using Retrieval Augmented Generation (RAG), vector and embedding methods can create security issues that traditional security checks might miss.
Attacks like embedding poisoning can retrieve harmful data, while manipulating the vector structure can bypass security filters. Additionally, flaws in how embeddings are handled can expose sensitive information.
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Unbounded Consumption: Unbounded consumption happens when an LLM application, like ChatGPT Pro, allows excessive use of computational resources. This can lead to problems like service outages, financial losses, model theft, and drained resources.
Attackers can take advantage of this by sending a large number of complex requests, overwhelming the system, causing disruptions, and increasing costs.
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Library Injection Exploit: Trojanized This is known as "supply chain vulnerabilities," where attackers create fake versions of libraries or LLMs and disguise them as trusted services. Users, unaware of the malicious code, might download and use these models, thinking they're legitimate.
After being integrated, attackers can control the model to access sensitive information or carry out unauthorized actions.
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Zero-Day Vulnerabilities: Zero Day Flags are serious security flaws in AI systems that attackers often find before anyone else, including the security teams. Since there’s no quick fix, these vulnerabilities can be taken advantage of until a solution is found and implemented.
This gives attackers a chance to exploit the weakness, potentially causing damage until a patch is released.
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Misinformation Generation: Misinformation is a big problem with LLMs, as they can produce content that seems true but is actually false or misleading. This can cause users to make decisions based on wrong information, leading to serious consequences.
AI is already being used to create fake stories, false statistics, and made-up explanations that sound believable. Some people are using fake accounts and AI to spread false content and influence public opinion, which is impacting important decisions and could lead to serious effects.
LLMs are powerful, but their vulnerabilities show why strong security measures are essential. Regular audits, strict input/output validation, and careful training data management can help reduce these risks and create safer AI systems. Check out these awesome resources linked below for more details:
https://owasp.org/www-project-top-10-for-large-language-model-applications/
https://yourgpt.ai/blog/growth/how-to-hack-large-language-models-llm
If you have some more good resources drop them on comments below.
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