Adversarial Threats to Large AI Models

  1. Adversarial Attacks: Large AI models are vulnerable to adversarial attacks, where an attacker can deliberately manipulate input data to cause misclassification or other undesirable behavior.
  2. Privacy Concerns: AI models may inadvertently reveal sensitive information about individuals if they memorize or overfit to training data, leading to privacy risks.
  3. Model Bias: Large AI models can encode and amplify biases present in the training data, leading to unfair or discriminatory outcomes.
  4. Resource Intensiveness: Training and deploying large AI models requires significant computational resources, which may lead to increased operational costs and environmental impacts.
  5. Distribution Shift: Large AI models may perform poorly when faced with data distributions that differ significantly from the training data, leading to potential safety and reliability issues.
  6. Robustness: Large AI models may be susceptible to input perturbations, such as noisy or corrupted data, leading to decreased robustness and potential safety hazards.





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