Protecting Traditional AI Models from Adversarial Attacks
Artificial intelligence (AI) is rapidly transforming our world, from facial recognition software authenticating your phone to spam filters safeguarding your inbox. But what if these powerful tools could be tricked? Adversarial attacks are a growing concern in AI security, where attackers manipulate data to cause AI systems to make critical mistakes. Gartner predicts that 30% of cyberattacks will target vulnerabilities in AI, either through manipulating training data, stealing the AI model entirely, or tricking it with deceptive inputs, highlighting the urgency of addressing these vulnerabilities.
Traditional AI models can be surprisingly susceptible to these attacks. Imagine a self-driving car mistaking a stop sign for a yield sign due to a cleverly placed sticker. A 2020 study by researchers at the University of California, Berkeley, found that adding just a few strategically placed stickers to a stop sign could trick a deep learning model into misclassifying the sign with a staggering 84% success rate . The consequences of such an attack could be catastrophic. But how exactly do these attacks work?
Adversarial attacks come in many forms, all aiming to manipulate an AI model's decision-making processes. Here are some common techniques:
- Adding imperceptible noise: Imagine adding minuscule changes to an image, invisible to the human eye, that completely alter how an AI classifies it. For instance, adding specific noise to a picture of a cat might trick a facial recognition system into identifying it as a dog.
- Crafting adversarial inputs: Attackers can create entirely new data points that an AI model has never seen before. These examples are specifically designed to exploit the model's weaknesses and force it to make a wrong prediction.
- Poisoning: In some cases, attackers might try to manipulate the training data itself. By injecting perturbations into the data used to train an AI model, they can influence the model's behavior from the ground up.
- Extraction: Attackers can try to steal or replicate the underlying model by querying it extensively and analyzing the responses. This attack tries to reverse-engineer the AI model, effectively “stealing” its intellectual property, leading to intellectual property theft.
- Inference: In some cases, attackers try to extract sensitive information from the model’s output. They try to analyze the model’s response to various inputs; attackers can infer confidential data, such as personal user information or proprietary data used in the training model.
The susceptibility of AI models to adversarial attacks varies depending on their architecture. Even models with millions of parameters can be fooled with cleverly crafted attacks.
Future of AI Security
The future of AI security is increasingly threatened by adversarial attacks, where AI models are deceived using manipulated data. To address this, Styrk’s AI security product, Edna, can be used to assess and enhance the robustness of AI models. It scans the labeled data and performs pre-selected adversarial attacks on it. After executing these attacks, the system identifies any vulnerabilities and reports them.
In addition to identifying adversarial attacks, Styrk also suggests defense mechanisms to help mitigate such threats. Once the client applies these defense mechanisms to the AI model, the mode can be re-scanned to detect any remaining vulnerabilities. The system then proposes further defense mechanisms to address the newly identified threats.
To keep the AI models safe, it becomes necessary to keep scanning it regularly as the attacks and defenses will keep evolving and increasing. We keep our product up to date by constantly adding new attacks and defenses to it, keeping us ahead of the curve in developing robust defenses. At Styrk, we anticipate and stop attacks before they happen and ensure that AI technology helps, not hinders, enterprises.
Making LLMs Secure and Private
Between 2022 and now, the generative AI market value has increased from $29 billion to $50 billion–representing an increase of 54.7% over two years. The market valuation is expected to rise to $66.62 billion by the end of 2024 and suggests a surge in companies seeking to integrate generative AI into their operations, often through tools like ChatGPT, Llama, and Gemini, to enhance and automate customer interactions.
While AI technology promises significant benefits for businesses, the growing adoption of generative AI tools comes with the risk of exposing users' sensitive data to LLM models. Ensuring the privacy and security of users’ sensitive data remains a top priority for enterprises, especially in the light of stringent regulatory requirements like EU AI Act to protect personal and financial data of its users.
To keep enterprise data secure while using the generative AI tools, Styrk offers multiple privacy-preserving mechanisms and a security wrapper that enables businesses to harness the power of generative AI models. This safeguards sensitive information and maintains compliance with data protection regulations.
Styrk’s core capabilities for LLM security
Not only can Styrk be used to protect sensitive data but also AI models from prompt injection attacks or filtering out gibberish text. Some of Styrk’s key capabilities:
- Compliance Monitoring: Styrk provides a compliance and reporting dashboard that enables organizations to track the flow of sensitive information through AI systems. Data visualization makes it easier to identify data breaches, adhere to regulatory standards, and, ultimately, mitigate risk.
- Prompt Injections: Styrk is equipped with mechanisms to filter prompt injections, safeguarding AI systems from malicious attacks or manipulation attempts. By mitigating the risk of prompt-injection vulnerabilities, LLM Secure enhances the security and resilience of AI-powered interactions, ensuring a safe and trustworthy user experience.
- Data Privacy Protection: Companies across various sectors can use Styrk to protect sensitive customer information before it is processed by AI models. For example, Styrk deidentifies personally identifiable information (PII) such as names, addresses, and account details to prevent privacy risks.
- Gibberish text detection: Styrk filters out gibberish text, ensuring that only coherent and relevant input is processed by AI models. Detecting gibberish text also helps in preventing any potential jailbreak or prompt injection attacks. This enhances the quality and reliability of AI-generated outputs, leading to more accurate and meaningful interactions.
The AI industry is rapidly growing and is already helping companies deliver more personalized and efficient customer experiences. Yet as businesses adopt generative AI into their operations, they must prioritize protecting their enterprise data, including sensitive customer data. Not only does Styrk enhance customer engagement, it enables regulatory compliance in a fast-moving landscape. Styrk prepares businesses to anticipate changes in AI and adjust their strategies and models accordingly.