Adversarial Training

Quick Assessment

Overview

Adversarial training is a technique for improving AI system robustness by training on examples specifically designed to cause failures. For language models, this primarily means training on jailbreak attempts, prompt injections, and other adversarial inputs so that models learn to handle these attacks appropriately rather than being fooled by them. The approach has become standard practice at all major AI labs as a defense against the most common and embarrassing failure modes.

The technique builds on extensive research in adversarial examples for neural networks, where small perturbations to inputs can cause dramatic misclassifications. Goodfellow et al. (2015) introduced the Fast Gradient Sign Method (FGSM) and demonstrated that neural networks' vulnerability to adversarial perturbations stems from their linear nature. Madry et al. (2018) established Projected Gradient Descent (PGD) adversarial training as the gold standard for robustness. For LLMs, adversarial training involves collecting examples of successful attacks (often from red teams or discovered in production), generating model responses to these attacks, and training the model to produce safe responses instead. This creates a feedback loop where new attacks are discovered, added to training data, and defended against.

However, adversarial training faces fundamental limitations. First, it creates an arms race: as models become robust to known attacks, attackers develop new ones, requiring continuous investment. Second, it only defends against attacks the system has been trained on - novel attack categories will still succeed. Third and most critically, adversarial training targets external attacks on the model, not internal model problems. It provides no protection against a deceptive or misaligned model, which could easily generate safe-seeming outputs while pursuing different goals.

Risks Addressed

Risk Assessment & Impact

The Adversarial Training Loop

Types of Adversarial Examples

Technical Implementation

The Arms Race Dynamic

Why Adversarial Training is a Race

Arms Race Implications

What Adversarial Training Defends Against

What It Doesn't Defend Against

Deception Robustness Analysis

Why Adversarial Training Doesn't Help with Deception

A deceptive model faces no challenge from adversarial training - it simply produces outputs that appear compliant while potentially maintaining misaligned internal goals. Adversarial training optimizes the model to handle adversarial inputs, not to have aligned objectives.

Scalability Analysis

Current Scalability

Future Scalability Concerns

Current Adoption & Investment

Differential Progress Analysis

Relationship to Other Approaches

Complementary Defenses

Key Distinctions

Best Practices

Effective Adversarial Training

Common Mistakes

Key Uncertainties & Research Directions

Open Questions

1. Is there a ceiling on adversarial robustness? Or will attacks always exist?
2. Can attack generation be automated effectively? Would change economics
3. How to generalize to novel attack categories? Currently weak point
4. What's the right balance with capability? Over-defense harms usefulness

Research Priorities

Sources & Resources

Primary Research

Related Reading

AI Transition Model Context

Adversarial training relates to the Ai Transition Model through:

Adversarial training is important operational security but doesn't address fundamental alignment challenges - it defends against external attacks while the deeper concern is internal model properties.