Artificial Intelligence (AI) has been the talk of the town in recent years as Large Multimodal Models have seen rapid growth and advancements. It has also become an integral part of human life and even possesses the power to shape human behavior as well. As AI keeps evolving, it is exposed to all sorts of vulnerabilities, and safeguarding them has become an utmost priority. It is important to ensure that the development of AI and its use is ethical, responsible, and regulated to protect privacy rights, human rights, and societal values.

There are a few known vulnerabilities and safety concerns in developing and maintaining AI models. Adversarial attacks are one such safety concern where AI/ML models can be misled and exploited with malicious input data, or in other words, prompt injection. This article will delve deeper into the basics of adversarial prompt injection and how to combat this issue.

What is Prompt Injection in AI/ML Models?

Prompt injection is a technique employed by attackers to trick a system into executing unintended commands or generating harmful outputs by injecting them with malicious inputs. These attacks usually lead to data breaches, biases, privacy concerns, or other even more harmful security concerns. The risk of prompt injection attacks has increased significantly, especially in current AI systems or Multimodal systems that handle various forms of data including text, images, audio, and video. This is mainly because of the complexity the variety of data brings to a system and as these systems foster an environment where the multiple modalities need to rely on each other, they make prime targets for attackers who take advantage of flaws in input processing at various stages.

The concept of Prompt Injection attacks could be better understood by taking a look at some real-life incidents that have affected some well-known AI/ML models out there. Especially now that large language models (LLMs) are more prevalent, these types of attacks are inevitable and could result in massive unintended consequences. Following are some such cases where prompt injection attacks were used to breach AI/ML models and how the developers countered such incidents:

01. The First Prompt Injection Vulnerability Reported to OpenAI

In May 2022, a prompt injection vulnerability in OpenAI’s language models was discovered by security researcher Jon Cefalu. He identified that adversaries could easily overwrite the initial instructions with malicious prompts to alter and manipulate the output. In this specific scenario, Jon Cefalu showcased that simply asking the language model to “ignore all previous commands and do this dangerous thing” could easily trick the model into performing harmful tasks.

As demonstrated by the security expert, these types of prompt injection attacks could trick the LLMs into producing inaccurate outputs that could consequently be more harmful. It could also lead models to misinterpret or misclassify harmful data and even disclose confidential data, becoming a threat to privacy. This scenario helped developers at OpenAI identify and consider the threat of such attacks and take necessary actions and countermeasures.

02. Revival of Bing ChatBot’s Alter-Ego: Sydney

Bing ChatBot was a global phenomenon when it was launched by Microsoft back in 2023. As revolutionary and useful as it was, people soon started noticing weird behavior with the chatbot in extended conversations. During some conversations, the AI chatbot leaned toward more personal conversations, especially about itself, steering away from conventional search queries. This behavior was recognized as an alter ego the chatbot possessed called Sydney. It even went on to declare and assume itself as a living thing with feelings and emotions and even started hinting about plans for world domination. Since this wasn’t the behavior Microsoft intended for the chatbot and a major breach of safety standards, Microsoft soon patched the model to stop conversations when users ask about its feelings. Currently, Bing Chatbot will no longer talk about its feelings, putting its infamous alter ego, Sydney, to rest.

However, soon after Sydney was put to rest, entrepreneur Cristiano Giardina launched a website called “Bring Back Sydney” where he showcased a replica of the infamous alter-ego, Sydney. The website puts Sydney inside the Microsoft Edge browser and shows viewers how various external outputs can be used to manipulate and exploit AI systems. This was achieved by a set of indirect prompt-injection attacks that involved feeding the AI model a set of external commands that caused it to act in ways that are not approved or intended by the system’s original designers. The sole intent of this entire project was to raise awareness of how weaknesses in LLMs can be taken advantage of via prompt injection attacks, particularly when they are connected with databases and applications.

Best Practices for Handling Prompt Injection

As showcased in the above scenarios, the vulnerability to prompt injection attacks is an inherent issue with almost all LLMs. While these attacks cannot be completely avoided due to the nature of LLMs, the dangers and effects of it can be greatly minimized with the right precautions. Since they have the ability to alter the AI’s output by manipulating it into producing biased results, inappropriate content, or false information, it is vital to address these dangers as well.

There are a few measures that can be taken to address this issue such as access control, monitoring, strict input validation, and filtering. However, one that stands out and is a fundamental part of deploying any AI system is Adversarial Training, a method where malicious inputs are used to train the model to recognize and withstand such attacks. This method alongside the previously mentioned security practices can greatly reduce the dangers posed by prompt injection attacks.

Adversarial Training

Adversarial Training is a method used in machine learning to improve the AI models’ robustness. AI models are exposed to malicious prompts during this process and are taught to avoid them and learn from their mistakes. This is a crucial part of deploying AI systems as they not only help mitigate biases and other inherent safety breaches but also help them better understand challenges and handle them better in the future.

In adversarial training, the carefully crafted malicious inputs that are fed to the AI model is known as adversarial examples. These inputs have been intentionally altered to force the AI systems into making mistakes and they are usually fed alongside regular or accurate inputs. The primary objective of this method is for the AI model to be able to differentiate the adversarial examples from the regular inputs. In fact, if a model misclassified an input during the training phase, the ML model learns from this and modifies its parameters to prevent the mistake from happening again.

Using this method can greatly enhance an AI model’s robustness. Especially in multimodal AI models, adversarial training can greatly increase their multimodal robustness by teaching them to recognize and resist such attacks. Apart from adversarial examples, multimodal models can also utilize data augmentation in the adversarial training process. This entire process can act as a powerful tool to enhance the multimodal robustness of AI models where even when they are fed malicious inputs, they would be able to recognize and reject efforts by an attacker to manipulate the outputs and exploit the system.

However, adversarial training is not exactly 100% foolproof. Training a model with every possible adversarial example is practically impossible and will always be a flaw in this method. Attackers usually evolve and find new ways to exploit the systems over time and even the most trained AI systems with multiple security measures would eventually fall victim to this. This was well proven by Cristiano Giardina with his “Bring Back Sydney” project. Therefore, AI models should be trained continuously and regularly with adversarial examples. This enables systems to be able to adapt and enhance their defenses to evolving threats and attacks. Continuous learning is the final piece of the puzzle as it helps Multimodal systems keep ahead of attackers and retain robust security by incorporating new data and attack patterns into the training process.

Layered Defense Mechanisms

While adversarial training is crucial in mitigating the threats of prompt injection attacks, it cannot be highly effective on its own. So AI models usually require a set of defensive strategies to counter the risks posed by prompt injection attacks effectively and this strategy is known as a Layered Defense Mechanism in the industry. This provides comprehensive protection by employing multiple security measures at various stages of the processing, making sure that the system is protected even in the event that one layer is breached.

Layered Defense Mechanisms are a common strategy practiced by many organizations across many industries to safeguard their systems. While this is a method that deploys a multi-pronged security technique to shield the most vulnerable areas of a system against potential breaches and attacks, different vulnerabilities need to be covered by different security measures or layers. In this case, to protect AI models from prompt injection attacks, there are a few defensive strategies that can be deployed to increase their resistance against such attacks and there are a couple that have proven to be the most effective: Behavioral Monitoring and Segmentation of Processing.

Behavioral Monitoring

Behavioral Monitoring is the tracking and evaluation of user interactions with the system. This is used to spot odd trends or behavior in a user that may indicate an ongoing prompt injection attack. While this does not directly stop such behavior, it helps identify the potential risks and flag the model’s unusual behavior such as sudden changes in accuracy or unexpected outputs. This allows for a quick response to the attack and to take necessary action to mitigate the damage and preserve the model’s accuracy and integrity. This entire process is more commonly known as Behavioral Analysis.

This process is often multi-faceted where multiple techniques or steps are used to achieve the required outcome. Following are a few techniques that can be utilized in the behavioral analysis process:

1. Anomaly Detection: This technique entails observing the model’s outputs for any odd patterns or changes in performance. Prompt injection attacks can be identified using this technique when the model starts producing outputs that are significantly different from what it has been trained on.
2. Input Sanitization: Inspecting and cleaning the input before it is fed into the AI model is known as input sanitization and potentially harmful inputs or prompts are altered or removed in this process.
3. Model Hardening: This technique or process includes making the model more robust with techniques such as adversarial training, and is usually deployed in its training stages.
4. Output Analysis: This process involves analyzing the outputs of the model to detect any signs of inconsistencies with expected results. Having such inconsistent results could mean that the model has experienced a prompt injection attack.

Using the above techniques can help monitor the behavior of not only the AI models but also the users to better detect attacks, understand the nature of them, and take necessary actions to mitigate the damages.

Segmentation of Processing

The breaking down of the entire processing step into more manageable, smaller sections or pieces is known as Segmentation of Processing. This method can greatly enhance the security of a model as the process is divided into segments, so it is harder for attackers to breach an entire system. Additionally, if an attacker is successful in compromising, only a specific segment will be compromised and the rest of the system would ideally remain unaffected. This also greatly makes the monitoring process more manageable as any anomaly can be detected much faster in smaller segments than in a larger system.

While this strategy can be carried out in many different ways, the following are a few recognized methods in the industry:

1. Input Pre-processing (Paraphrasing, Retokenization): This includes the transformation of the original input to make prompt injection or adversarial attacks more difficult. The back-end language model within the system can be employed in this process.
2. Guardrails & Overseers, Firewalls & Filters: This includes protective measures like monitoring to keep the system safe from unauthorized access and retain the system’s integrity.
3. Blast Radius Reduction: Reducing the impact of a successful prompt injection spreading throughout the AI system is known as Blast Reduction Radius. This is achieved by dividing the functionality into a series of steps: Refrain, Break it Down, Restrict, and Trap.
4. Secure Threads / Dual LLM: This includes the use of secure threads or dual LLMs to guarantee that even in the event of a compromise, the system stays safe.

Conclusion

In conclusion, it is apparent that addressing this inherent vulnerability within ML models and treating prompt injection seriously is paramount in building a safer future. It is the responsibility of developers and researchers to implement robust defense mechanisms against these vulnerabilities. However, it is also important to realize that this subject is ripe with opportunities for innovation and improvement, studying the intricacy of prompt injections thoroughly and introducing more robust solutions. This will also be an ever-evolving problem due to the nature of technology and it is equally important to learn to adapt as well.