Training Data Poisoning: Complete Defense Framework -Quiz

Why: This is an integrity attack (backdoor) because the model performs normally except when a specific trigger pattern is present – the shirt pattern activates the hidden backdoor. Context: The attack passed all standard accuracy tests because backdoors are designed to be invisible until the specific trigger appears. Remember: Works perfectly except for one hidden trigger – that is a backdoor.

Why: The Pravda network created approximately 3.6 million articles specifically designed to be scraped by AI training pipelines to inject specific narratives and biases into language models. Context: This attack exploited the fundamental vulnerability that most large language models are trained on web-scraped data with no reliable way to verify authenticity at scale. Remember: Pravda – 3.6 million fake articles to poison the entire web training pipeline.

Why: Differential Privacy adds calibrated noise to gradients during training, limiting the influence of any individual sample including poisoned ones – it is the only mathematically proven defense at scale. Context: While it may slightly reduce model accuracy, for high-risk applications this trade-off is usually worthwhile. Remember: Differential Privacy – mathematically proven, accepts accuracy trade-off for security.

Why: Unlike traditional security threats that can be patched, training data poisoning requires retraining the entire model to fix because the corruption is baked into the model weights. Context: You cannot simply remove malicious data after training – the model has already learned from it and encoded those patterns permanently. Remember: You cannot patch poisoning – only retrain from scratch.

Why: A sleeper attack is a backdoor designed to activate only after a specific date, keyword, or condition is met – remaining dormant through testing and production. Context: These attacks can pass extensive testing and months of production use because the trigger may be a future date or rare phrase that never appears during evaluation. Remember: Sleeper attacks are time-bombs – they wait until the attacker chooses to strike.

Why: Integrity attacks create hidden backdoors that let attackers control the model while it appears normal in all other cases – the model passes all standard accuracy tests. Context: The attack is invisible until the attacker activates the specific trigger, which is why normal testing and monitoring will not detect it. Remember: Backdoors pass all tests – the model works perfectly until the attacker wants it not to.

Why: Availability attacks aim to degrade model performance by injecting noise, conflicting labels, or misleading patterns that confuse the model during training. Context: These attacks make the AI unreliable and may look like poor training rather than a security incident, making them easy to misattribute. Remember: Availability attacks break the model – it works poorly for everyone.