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Of all the attack vectors that AI models are subject to, data poisoning is probably the most insidious, because it takes place before an LLM is fully developed or deployed. This type of cyberattack happens when malicious actors tamper with the training data that makes up the model’s input, in order to manipulate its output.
This tampering can take the form of inserting, modifying, or deleting data from a training data set. Data that’s poisoned in this way that can make models less trustworthy, or even outright harmful.
In this article, we take a detailed look at the most critical data poisoning attack vectors, and practical steps organizations can take to improve their security posture.
Data poisoning can proceed in a targeted, or non-targeted manner, each with its own tell-tale signs and security priorities.
An adversary with a highly specific objective will often target a particular file or piece of training data. These attacks typically don’t cause immediate performance degradation.
On the other hand, non-targeted attacks intend to disrupt a model’s functioning in a more general way. This tends to have a more noticeable impact on model functioning.
Not necessarily. Data poisoning attacks affect both private and public data sets.
Private data sets can be infiltrated either through sophisticated hacking, or an insider. Public data sets, often used for training large-scale AI models, are vulnerable to more widespread attacks. Attackers can inject poisoned data into publicly accessible repositories, such as open-source databases or web-crawled content, affecting numerous models and applications that rely on this data.
Cybercriminals are nothing if not inventive. Here are some of the methods they’re using to tamper with training data to manipulate AI models.
DoS attacks aim to overwhelm the AI model by feeding it poisoned data. In the process, such an attack can make the model crash, or render it unusable for a period of time. These attacks lead to significant performance degradation and potential downtime.
For example, an attacker could flood a spam detection system with a large volume of mislabeled emails, disrupting its ability to function and rendering it ineffective at filtering spam.
Chatbot LLMs use data from human-model interactions, which makes them susceptible to attacks that resemble social engineering, such as jailbreaking (or direct prompt injection).
In this type of attack, a malicious actor manipulates the AI model with instructions that neutralize or overwhelm the LLM’s restrictions and safety features. Attackers continue to develop new methods for introducing poisoned inputs to get around LLM defenses.
Attackers with a deep knowledge of how LLMs are trained can use that insight to reverse-engineer a model and reveal proprietary information through sophisticated prompting. In this type of attack, the attacker infers data by identifying patterns in the model’s output.
These attacks pose a serious risk to the confidentiality of customer information, or product specifications that are not publicly available.
Stealth attacks subtly alter the AI model's behavior without raising obvious red flags. An attacker might introduce slight modifications to a facial recognition system's training data, causing it to consistently misidentify certain individuals while appearing to function normally otherwise. This makes it difficult to detect the poisoning of the data, even while it achieves the attacker’s objectives.
In addition to the immediate threats we saw earlier, data poisoning can have broader, more long-term effects on AI systems, encompassing accuracy, security, fairness, and legal implications.
It’s wild out there - and you don’t want to be caught on the backfoot. Here are some of the techniques that companies should already be implementing to defend against data poisoning attacks.
Regularly sanitize and preprocess your datasets to remove anomalies and inconsistencies, ensuring only high-quality data is used for model training. This includes techniques such as outlier detection, normalization, and noise reduction to maintain data integrity.
Prevention is always better than cure: restoring or sanitizing corrupt data after the fact is impractical, and in some cases, it isn’t even possible. Implement data validation techniques at the outset, then track data provenance to maintain a history of data origins and transformations. Use the right tools to automate the heavy lifting here.
Data separation is necessary to isolate sensitive datasets from less critical ones. This prevents cross-contamination and preserves the integrity of critical data.
Strict access control measures limit who can view and modify datasets. Regular audits of access logs can also help identify when someone tries to access the model without authorization.
Malicious actors are smart and unrelenting. Your security teams should be, too. Preventing data poisoning attacks requires continuous monitoring and auditing for signs of corruption. Continuously monitor and audit data and models for signs of poisoning, utilizing advanced anomaly detection algorithms and regular audits.
Design robust model architectures that can withstand adversarial attacks, incorporating techniques like adversarial training and robust loss functions, and perform comprehensive model validation using clean and trusted datasets. Ensuring model resilience from the outset reduces the impact of any potential poisoning attempts, and regularly evaluating model performance on separate validation sets helps detect unexpected behavior early.
Validate and verify all inputs to the model to ensure they meet expected standards, using automated tools to detect and filter out malicious inputs while incorporating data from diverse sources to reduce the risk of poisoning. Ensuring all sources are verified and trustworthy helps create more robust models that are less susceptible to single-source attacks.
All employees, users, and stakeholders need to understand the risks of data poisoning. But this is new territory for everyone, so training and education are vital. Companies should offer regular training sessions and updates to give users the tools to identify threats - and, even better, to use AI models in a way that makes it difficult for malicious actors to get a foothold to begin with.
Ensuring the integrity of your GenAI systems is crucial in the face of data poisoning threats. Lasso Security provides robust protection by detecting and mitigating poisoning attempts in real time. With our cutting-edge security suite, Lasso offers our customers the ability to monitor data inputs, and outputs and identify anomalies, Lasso Security keeps your training data clean and your GenAI models reliable.
The platform's comprehensive reporting and adaptive learning capabilities ensure ongoing protection against emerging threats. Lasso Security not only safeguards your data but also maintains the trustworthiness and effectiveness of your GenAI applications.
