Data Poisoning attacks on Enterprise LLM applications: AI risks, detection, and prevention

Introduction

Imagine discovering a backdoor in your company’s data-driven decision-making process, where maliciously altered data subtly skews outcomes. This manipulation can be used to reveal confidential information that the model has access to. Additionally, it can trigger misleading outputs for some or all of your observations, leading to erroneous conclusions or decisions based on compromised data

This scenario is not just a hypothetical; it's an emerging reality in the age of Large Language Models (LLMs). As AI becomes more integrated into business operations, especially in sectors like finance, healthcare, and retail, the risks multiply. Data poisoning—where critical data is tampered with to manipulate AI behaviors—poses a unique cybersecurity challenge that can go unnoticed until substantial damage has occurred. This post aims to demystify this challenge, illustrate the risks involved, and showcase how you can safeguard your LLM initiatives, with a special focus on how Giskard can help you both detect and mitigate this threat.

What is Data Poisoning? Understanding AI Security risks and model poisoning

Threats of Data Poisoning in LLMs

Data poisoning is similar to the tactics used by advanced spammer groups attempting to manipulate Gmail's spam filter. These groups deliberately mark massive volumes of spam emails as not spam, aiming to disrupt the filter’s accuracy. In a parallel way, data poisoning targets the core functionality of Large Language Models (LLMs) by corrupting the data they depend on for learning. This type of cyberattack affects the training datasets that are crucial in shaping the model's understanding and outputs, skewing decision-making processes and compromising the integrity of its responses. For a detailed discussion of the Gmail filtering attacks, refer to this article.

When malicious actors inject false data into these datasets, they subtly manipulate the LLM’s behavior. The altered data can be designed to trigger specific responses or biases in the LLM, serving the perpetrator's hidden agendas.

The mechanics and impact of Data Poisoning on LLMs

Data poisoning can target LLMs that businesses use for automating customer service, analyzing sentiment, or managing supply chains. By compromising the integrity of the training data, adversaries can manipulate these models to misinterpret text, generate misleading responses, or fail at critical tasks designed to automate complex decision-making processes. Unlike most LLM-related security issues that only affect the attacker's session, a data poisoning attack impacts the model itself—altering its behavior across all user interactions. This widespread effect makes data poisoning particularly perilous, as it not only disrupts the intended functionality for all users but also undermines trust in the model's reliability and security.

Undetected Data Poisoning in AI models: How it happens

Data poisoning in LLMs often slips past security measures due to the subtlety of tampering and the vast volumes of data these models process. LLMs, trained on enormous datasets, face significant challenges in data management and verification, making it difficult to spot anomalies before they influence the model's behavior. This issue is compounded by the complexity and variety of data sources involved, which can obscure the origins and integrity of the input data. The insidious nature of this manipulation means it can often proceed undetected for long periods, buried under the sheer volume of data that LLMs train on, making it a sophisticated form of adversarial AI.

Detecting Data Poisoning in enterprise AI applications

LLMs in businesses may exhibit subtle yet distinctive symptoms when afflicted by data poisoning. For example, in financial firms, a poisoned LLM might begin generating financial reports with slight inaccuracies or unusual predictions that don't align with known market conditions. These aberrations, if unnoticed, could lead to significant financial misjudgments or compliance issues.

In the automotive industry, LLMs used for processing customer feedback or predicting market trends might start displaying biases in output, or their language processing could suddenly misinterpret critical feedback, leading to flawed strategic decisions or overlooked customer concerns.

These symptoms—subtle shifts in output accuracy, the emergence of biases not previously detected, or unexplained changes in the performance of automated systems—are critical red flags. They suggest that the underlying data driving AI decision processes may have been compromised, warranting a thorough investigation.

Real-World examples of Data Poisoning Attacks

Data poisoning not only compromises the integrity of AI systems but also poses significant financial, reputational, and operational risks to businesses. Understanding its mechanisms and manifestations is crucial for safeguarding enterprise Large Language Models (LLMs) applications. To illustrate the severity and real-world impact of these threats, let's examine some specific instances where data poisoning has tangibly affected companies.

Prompt Injection

A severe incident occurred when a GPT-3-based Twitter bot, run by a recruitment startup called Remoteli.io, was compromised through a prompt injection attack. Malicious inputs were cleverly introduced into the bot's operation, causing it to leak its original prompt and generate inappropriate responses to discussions about "remote work." This breach not only disrupted the startup's ability to communicate effectively on social media but also posed significant reputational and legal risks. The event highlights the critical vulnerabilities in AI systems and the profound implications such attacks can have on business operations and public trust.

Malicious Code Injection

In a concerning security breach, attackers compromised AI-based code generators by injecting malicious code during the training data phase. This manipulation occurred when attackers uploaded 100 poisoned models to the Hugging Face AI platform, where the corrupted models could potentially inject malicious code into user systems. The incident highlights a critical supply chain vulnerability, as these poisoned models could be unwittingly incorporated into other systems and applications. The breach has underscored the urgent need for enhanced security measures and thorough vetting of AI models to prevent similar incidents in the future, protecting users from substantial security risks.

Output Manipulation

Output manipulation in the case of New York attorney Steven Schwartz using ChatGPT was clearly demonstrated during the legal proceedings of Mata v. Avianca. Schwartz utilized the AI for legal research, expecting accurate assistance. However, the AI erroneously generated and included fabricated legal citations and case law in its responses. This error, stemming from the AI's processing and generation mechanisms potentially influenced by its training data, constitutes a form of output manipulation where the AI’s output was misleadingly altered, leading to incorrect legal documentation. The reliance on these flawed AI outputs without adequate verification brought severe professional consequences for Schwartz and raised significant concerns about the reliability of AI in legal research. This misuse not only sparked a judicial controversy but also initiated a wider dialogue on the ethical implications of deploying AI tools in critical and sensitive decision-making fields.

The Case of Unsanitized Scraped HTML

Consider the scenario where a company uses web scraping to gather large volumes of text data to train a customer support chatbot. If the HTML content scraped from the web is not properly sanitized, it may include malicious scripts or misleading metadata that are not immediately evident. For example, a seemingly innocuous blog post might contain hidden HTML comments or script tags that, when processed by the LLM, are interpreted as legitimate data. This can lead to the injection of malicious prompts in the LLM's training dataset.

For instance, an unsanitized script tag in a scraped webpage could carry a payload that subtly alters the chatbot's responses to favor a specific product or service, or worse, generate responses containing spam or phishing links. Because these injections can mimic normal variations in data, they might not raise immediate red flags during the model training phase, leading to their integration into the LLM's operational framework. The result is a compromised model, subtly trained to execute tasks that align with an attacker’s specific agenda. This manipulation can persist undetected, gradually impacting business decisions and customer interactions until significant anomalies prompt a closer inspection.

Safeguarding LLMs against Data Poisoning: Best practices in AI Security

Mitigating the threat of Data Poisoning

To protect Large Language Models (LLMs) from data poisoning, you must implement sophisticated, AI-specific security measures. 

• Stringent Data Validation Protocols: Ensure each training dataset undergoes thorough scrutiny for integrity and authenticity. Check data for accuracy and relevance and ensure it is free from manipulation.
• Regular Audits and Updates:
  • Audits (such as Red Teaming): Identify subtle discrepancies that might indicate tampering.
  • Updates: Ensure AI models incorporate the latest security features and adapt to new threats.
• Layered Security Strategy:
  • Software Solutions: Implement software that enhances security.
  • Operational Protocols: Include encrypting data sources, implementing access controls, and segregating training data from operational data to prevent unauthorized access and contamination.
• Employee Education: Train employees on AI security and the signs of data poisoning to further strengthen defenses.

By adopting these robust security practices, you can strengthen your LLMs against data poisoning, protect your technology, and keep your AI-driven solutions trustworthy and reliable.

AI Red Teaming to enhance Security against Data Poisoning

One effective strategy is the use of Red Teaming, where a group of security experts (the “Red Team”) adopt the mindset and tactics of potential attackers. This team deliberately attempts to exploit vulnerabilities in the LLMs and their training datasets, providing a realistic simulation of potential threats. The insights gained from these exercises enable businesses to strengthen their defenses against actual cyber threats, ensuring that their AI systems are resilient against sophisticated data poisoning attacks.

At Giskard, our Red Teaming experts have helped businesses like Axa and Michelin to uncover vulnerabilities in their AI systems and training datasets. Through realistic simulations of potential threats, our team identifies weaknesses and provides actionable insights to strengthen defenses against actual cyber threats.. This allows businesses to ensure that their AI systems are resilient against data poisoning attacks, enhancing overall security and mitigating potential risks.

Automated LLM evaluation for detecting Data Poisoning

Automated tools and algorithms for evaluating LLMs are key to maintaining AI systems' security and integrity by detecting signs of data poisoning and other threats. These tools can automatically analyze large volumes of data, identify anomalies or suspicious patterns, and alert stakeholders to potential risks in real-time. Additionally, they help to ensure compliance with regulatory requirements and industry standards by continuously monitoring model performance and adherence to established guidelines.

The Giskard python library offers an LLM scan functionality, which combines both heuristics-based and LLM-assisted detectors to probe LLM systems for vulnerabilities, providing you with an additional layer of protection against data poisoning and other security threats.

Conclusions

The thread of data poisoning is a critical issue when you are deploying your LLM based applications in your company. To effectively counter the risks posed by malicious actors subtly corrupting the data that feeds our Large Language Models (LLMs), robust security measures and vigilant monitoring are essential, including implementing stringent data validation protocols, regular model audits, and employing advanced defensive strategies like Red Teaming.

Giskard specializes in offering comprehensive testing frameworks tailored for LLMs and other AI models. Our solutions empower businesses to assess their models, ensuring the accuracy, fairness, and safety of their AI applications. Contact us today to discover how we can optimize your AI investments.