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Sometimes you want to train or use an AI without having all the work and cost of training it from scratch. In these cases it’s common to reach for an open source model. Sites like Hugging Face allow people to share models and datasets without being dependent on just those used by the monolithic corporations.

Nowadays, most AI and Machine Learning algorithms leverage large amounts of data which can be purchased, collected or sourced online. This data is known as the Training Dataset and it enables the model to learn patterns and relationships within the data. By doing so, the model can make predictions or decisions based on new and unseen data.

Supply Chain attacks in cybersecurity exploit businesses’ reliance on these third-party components, vendors, or suppliers to gain access to their primary target – the company itself.They look to break into companies, not by targeting them directly, but rather the less secure elements in their supply chain. This sort of attack is common as any technology becomes widespread.

Malicious Actors can explore the way AIs identify or generate content and try to maximize certain factors to produce unintended behaviors. This kind of manipulation is called Adversarial Patching.While Data Poisoning alters the training data to produce strange results, Adversarial Patching takes advantage of how an AI already is, there’s no necessary change.

AI’s ability to learn from vast datasets is its greatest strength but this capability can be turned against it and exposing private information is a reality even for the more complex models. Lets explore the mechanics of Model Inversion (MI) attacks, their methods, and strategies to mitigate them.

Membership Inference Attacks (MIA) expose the complicated relationship between training data and the model outcomes. Unlike model Inversion, which tries to reconstruct part of the training data without knowing it, MIA uses it to infer if a specific piece of data was used during training.

Generative AI is everywhere. It has become so widespread that it is present on virtually every industry and its adoption is still growing and we are already seeing LLMs being used in mass. Software companies are introducing them as a production multipliers. Doctors use them to summarize information about patients allowing them treat even more patients.

In the context of AI, misalignment is the divergence between an AI’s objectives and the values or goals intended by its human developers or operators. When an AI model is misaligned, it might pursue outcomes or behaviors that are unintended, undesirable, or even harmful.