Chain of Thought (COT)
A method for unlocking reasoning capabilities in large language models. By encouraging step-by-step thinking. COT prompting lets models perform complex reasoning tasks effectively without additional training data. The benefits are particularly pronounced in large models (e.g., models with over 100 billion parameters), which exhibit improved reasoning capacities as they follow these structured reasoning prompts. Source Paper
Frequently Asked Questions
What is an adversarial attack in AI?
An adversarial attack is a deliberate manipulation of an AI model’s input to cause a specific wrong output. Unlike conventional cyberattacks, adversarial attacks exploit the statistical properties of machine learning models rather than software vulnerabilities in code.
How are adversarial attacks used against security tools?
Attackers use evasion techniques to modify malware, network traffic, or phishing content so that ML-based classifiers label it as benign. The malicious payload remains functional. The security tool fails to detect it. Adversarial evasion of endpoint detection, spam filters, and network intrusion systems has been demonstrated in research and in production environments.
What is the difference between evasion and poisoning attacks?
Evasion attacks occur during inference, modifying inputs to fool a trained model. Poisoning attacks occur during training, corrupting the training data to embed a backdoor. Evasion exploits the model as it exists. Poisoning shapes the model before it is ever deployed.
How should organizations defend against adversarial attacks?
Layer defenses rather than relying on any single ML-based control. Apply adversarial robustness testing before deployment, including attempts to find evasion inputs against your classifiers. Monitor model performance on recent real-world data, not only historical benchmarks. Treat the training pipeline as an attack surface and audit data sources before training begins. Follow OWASP GenAI Security Project guidance on prompt injection for any LLM-powered system.
