Adversarial Attacks
Targeted input manipulations that cause AI systems to misclassify or behave incorrectly.
Adversarial attacks deliberately manipulate AI inputs to force misbehavior: invisible image changes, text tricks, prompt manipulation. Foundation of AI security research.
Explanation
For images: Invisible pixel changes fool classifiers. For text: Typos, Unicode tricks, synonyms. For LLMs: Prompt injection, jailbreaks. White-box attacks know the model, black-box only outputs.
Marketing Relevance
Marketing AI is vulnerable: Bypass spam filters, trick content moderation, manipulate chatbots. Adversarial testing is mandatory before production.
Example
An image classifier recognizes a "Stop" sign as "Speed Limit 80" after applying a small sticker – dangerous for autonomous driving.
Common Pitfalls
Adversarial robustness is expensive to train. New attacks constantly emerge. Robustness can cost accuracy.
Origin & History
Goodfellow et al. demonstrated adversarial examples in neural networks in 2014. FGSM (Fast Gradient Sign Method) became standard attack. LLM-specific attacks like prompt injection followed in 2022.
Comparisons & Differences
Adversarial Attacks vs. Prompt Injection
Adversarial Attacks is the umbrella term; Prompt Injection is a specific form for LLMs using natural language.
Adversarial Attacks vs. Data Poisoning
Adversarial attacks manipulate inputs at inference time; Data Poisoning poisons training data before training.