Today, I want to demonstrate an offensive security technique against machine learning models known as training data poisoning. This attack is classified as LLM03 in OWASP's TOP 10 LLM.
The concept is straightforward: if an attacker gains write access to the datasets used for training or fine-tuning, they can compromise the entire model. In the proof of concept I developed, I use a pre-trained sentiment analysis model from Hugging Face and fine-tune it on a corrupted, synthetic dataset where the classifications have been inverted.
Links to the GitHub repository and the Colab notebook can be found here: https://github.com/R3DRUN3/sploitcraft/tree/main/llm/dataset-poisoning/sentiment-analysis-poisoning
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from hacking: security in practice https://ift.tt/clNQ5DY
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