Uncover key vulnerabilities in 2024’s Large Language Models and the critical need for cybersecurity strategies to protect AI-driven systems.
In a plot twist straight out of a futuristic novel, Large Language Models (LLMs) have taken the world by storm over the past few years, demonstrating the agility of an improv artist and the depth of a seasoned scholar.
These silicon sages, armed with terabytes of text and algorithms sharp enough to slice through the densest topics, have turned mundane queries into epic tales and dull reports into compelling narratives. That precisely explains why nearly 65% of organizations have reported using AI in at least one business function, with LLMs playing a prominent role in this adoption, according to a recent McKinsey survey.
But are LLMs that fool-proof? Well in June we posted a blog article showcasing how LLMs fail to answer simple questions like counting the number of ‘r’ in the word strawberry.
So, what’s the catch? Are LLMs dumb? Or is there more than meets the eye? And most importantly—can these vulnerabilities be exploited by malicious actors?
Let’s find out. These are the Top Five ways in which LLMs can be exploited:
Data Inference Attacks
By observing the outputs of an LLM in response to specific inputs, hackers may extrapolate sensitive details concerning the model’s training dataset or its underlying algorithms, using it to mount further attacks or exploit weaknesses in the model’s design. There are multiple ways to do this:
Attackers may use statistical techniques to analyze the model’s responses to infer patterns or sensitive information that the model may inadvertently leak. They may also exploit fine-tuning capabilities if they have access to the model’s parameters. By adjusting the model’s behavior, cyberthieves can potentially increase its susceptibility to revealing sensitive data. Under the data inference attacks umbrella, adversarial inputs represent another avenue, where attackers intentionally design inputs to prompt specific responses from the model. Membership inference is another tactic where attackers seek to determine if a particular sample was part of the dataset used to train the model. Successful inference could yield insights into the training data, potentially exposing sensitive information.
Backdoor Attacks
One of the primary complexities associated with backdoor attacks on LLMs is their ability to remain dormant until activated by specific input patterns, rendering them challenging to identify through conventional means. For instance, an attacker might inject biased input into the training data, leading the model to generate responses favoring certain agendas or producing inaccurate outputs under predefined circumstances.
Model Denial of Job
Denial of Service (DoS) attacks against models focus on compromising the availability of LLMs by either bombarding the models with an overwhelming number of requests or exploiting vulnerabilities to induce a system failure. Examples of such vulnerabilities include continuous input overflow and variable-length input flood. This not only diminishes the quality of service and impacts users, it also may result in significant resource expenses.
This issue is exacerbated by the widespread adoption of LLMs across various applications, their resource-intensive nature, the unpredictable nature of user input, and a general lack of awareness among developers regarding this vulnerability.
Insecure Output Handling
Neglecting thorough validation of LLM outputs before acceptance can leave backend systems vulnerable to exploitation. This oversight opens the door to a range of serious consequences, including but not limited to cross-site scripting (XSS), cross-site request forgery (CSRF), server-side request forgery (SSRF), privilege escalation, and even the remote execution of malicious code.
An aspect of insecure output handling involves LLMs unintentionally revealing confidential details from its training data or inadvertently leaking personally identifiable information (PII) in its responses, potentially violating privacy regulations or exposing individuals to risks such as identity theft.
Training Data Poisoning
This vulnerability involves the deliberate manipulation of training data or fine-tuning data to introduce vulnerabilities, such as backdoors or biases, which can compromise the security, effectiveness, or ethical integrity of the model. These vulnerabilities, each with their unique or sometimes overlapping attack vectors, pose risks like performance degradation, downstream software exploitation, and damage to reputation.
Even if users are wary of the problematic outputs generated by AI, the risks persist, potentially leading to impaired model capabilities and harm to brand reputation. Examples of such vulnerabilities include unsuspecting users inadvertently injecting sensitive or proprietary data into the model’s training processes, which then manifests in subsequent outputs.
“Security teams cannot keep up with the operational tasks they must do each day, despite years of investment in in-house automation and tools to make them more effective – which is why we founded Simbian,” said Ambuj Kumar, Simbian Co-Founder and CEO. “Simbian puts the security operator firmly in charge of security decisions, and we enable the user to interact with products across vendors to get things done. We stand unique in the industry with our ability to generate commands in code using LLM and based on a natural language user interface, and we enable users to craft permutations of the actions we support, all on the fly.”
Exploiting LLMs used for cybersecurity
LLMs can accomplish tasks that were out of reach of machines in the past, and practitioners in every field have rushed to embrace LLMs in their daily lives. ChatGPT was the fastest growing consumer application in the first few months after release. At the same time, users are advised to be aware of the risks described above, and to sanitize all inputs to and outputs from the LLMs they use to protect themselves from being an unsuspecting victim.
Nowhere is this more important than in cybersecurity. Over the last year, security products have embraced LLMs to enable natural language inputs and outputs, and to make recommendations for next steps. If done well, this has tremendous potential to speed up cybersecurity teams. On the other hand, if not done carefully, it can make a security issue far worse. For example, consider if a new security analyst is using LLMs to understand how to respond to an alert of anomalous network activity, and receives a bad recommendation to open network ports.
Good News Regarding LLM Vulnerabilities
While LLMs have revolutionized various industries with their remarkable capabilities, it’s crucial to acknowledge the inherent vulnerabilities they possess. It is possible, in fact, to mitigate the above risks with the right practices. New LLM vulnerabilities are being discovered daily, so applications using LLMs need to be updated regularly. As long as you are disciplined in your updates, the benefits of LLMs far outweigh the risks.
As a result, it is highly recommended that when purchasing an LLM-powered solution, you review what the vendor is doing to use LLMs safely. This is especially important when purchasing an LLM-powered cybersecurity solution. Ask the product vendor what are they doing against the risks described above? Have they built deep expertise themselves in response to LLM risks, or are they paying lip service? And finally, LLM vulnerabilities is a moving field, so what are they doing to stay on top of the latest LLM vulnerabilities?
As hackers continue to explore innovative ways to exploit these vulnerabilities, the need for heightened awareness and robust security measures becomes paramount. By staying informed about potential threats and implementing proactive strategies to mitigate risks, organizations can safeguard their LLMs against malicious attacks.
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