Business Security
Hey there! Have you ever stopped to consider the trustworthiness of your AI assistant? Well, database poisoning can actually change its output in significant ways, and not always for the better – it could even pose a danger!
30 Jan 2025
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4 min. read
Hey tech enthusiasts! It’s no secret that modern technology isn’t foolproof – with vulnerabilities popping up left and right. While focusing on creating secure-by-design systems is important, it often means diverting resources from other crucial areas like user experience design, performance optimization, and interoperability.
That’s why security sometimes takes a backseat and only meets minimal compliance requirements. However, when sensitive data is involved, proper protection is vital. Particularly in AI and machine learning (AI/ML) systems, where data forms the core of their functionality.
What exactly is data poisoning?
AI/ML models rely on training datasets that continuously evolve through supervised and unsupervised learning. The more diverse and reliable the data, the better the model’s outputs. But this reliance on vast amounts of data brings risks – unverified or poorly-vetted datasets can lead to unreliable outcomes. Generative AI, especially large language models (LLMs) like AI assistants, are vulnerable to attacks that manipulate the models for malicious purposes.
One major threat is data (or database) poisoning, where adversaries aim to alter the model’s behavior to produce incorrect, biased, or even harmful results. The repercussions of such tampering can be far-reaching, affecting trust and introducing systemic risks.
What are the types of data poisoning attacks?
There are several types of data poisoning attacks, such as:
- Data injection: Attackers insert malicious data points into the training data to change the AI model’s behavior. Remember the Tay Twitter bot incident?
- Insider attacks: Employees with access could modify a model’s training set to alter its behavior, exploiting their legitimate access.
- Trigger injection: This attack inserts data into the training set to create a trigger, allowing manipulation of the model’s output under specific conditions.
- Supply-chain attack: Vulnerabilities introduced during the supply chain process can compromise the model’s security.
As AI models become integral to business and consumer systems, attacks on these systems are a growing concern.
While enterprise AI models may not share data externally, they still handle internal data, making them high-value targets. Consumer models, on the other hand, often share users’ prompts, containing sensitive data, with other parties.
How can ML/AI development be secured?
Strategies for securing ML/AI models include constant checks and audits of datasets, focusing on security in development, adversarial training, and zero trust and access management to defend against threats.
Secure by design
Building secure-by-design AI/ML platforms is imperative to prevent biased, inaccurate, or vulnerable outcomes. As AI becomes more ingrained in our lives, securing these systems is crucial for unlocking AI’s potential without compromising security, privacy, and trust.
