## 2 Related Work  
Web Application Firewalls (WAFs) are one of the most widely used defense systems against SQL injection (SQLi) attacks (See Section 3 for a brief background on WAFs). WAFs utilize two common strategies to defend against SQLi attacks--  
1. The rule-based approach detects attacks based on known attack signatures. Rules are typically written as a sequence of regular expressions designed to detect many forms of attack. Rule-based methods enjoy the advantages of being deterministic and transparent, having low false positive rates, are quickly deployable, being compliance friendly, and are resource efficient. However, they are limited to known patterns, require regular updates, are unable to adapt to slight variations in attack patterns, and have scalability issues. 2. The machine-learning-based approach detects attacks by learning from data to identify anomalous or malicious patterns in SQL queries. These methods typically involve training models on historical data, including both benign and malicious queries, to classify or detect suspicious behavior. Machine-learning-based methods enjoy the advantages of being adaptable to novel or obfuscated attack patterns, detecting deviations from normal behavior rather than relying on static signatures, improving continuously with more data, and reducing the need for explicit rule maintenance. However, they have drawbacks such as high false positive rates in poorly trained models, being resource-intensive, having complex and non-transparent decision-making processes, depending heavily on the availability and quality of labeled data, and requiring significant time and effort for initial deployment and fine-tuning. [PERSON] et al. [13] and [PERSON] et al. [14] present a review of literature that employs machine-learning and deep-learning techniques to detect SQLi attacks. Since our work focuses on enhancing WAF robustness through a generative AI-driven framework capable of generating novel SQLi attacks and updating existing WAF rules,we review related work in the areas of SQLi attack generation, rule adaptation, and WAF robustness improvement. [PERSON] et al. [15] propose a technique that transforms an original SQLi payload into generated SQLi payloads that can bypass WAFs through the use of Context-Free Grammar (CFG) for generation and a Monte Carlo tree search for selection. [PERSON] et al. [16] address the challenge of unevenly distributed effective attack payloads by leveraging Context-Free Grammar (CFG) to describe attack patterns and decomposing payloads into tokens. Feature vectors are extracted using label cleaning, and cosine distance metrics are employed to measure similarity between payloads. By adaptively selecting and executing promising payloads based on these metrics, they reduce unsuccessful attack trials. [PERSON] et al. [17] use a series of mutation operators, such as case swapping, whitespace substitution, and comment injection, to iteratively generate new payloads, starting with a malicious payload that is detected by the WAF. These mutation operators preserve the semantic integrity of the payloads, ensuring they remain functionally identical from the adversary's perspective. However, these transformations gradually reduce the confidence of the WAF's classification algorithm. This process continues until the payload's classification falls below the rejection threshold, resulting in successful SQLi attacks. [PERSON] et al. [18] propose an automated approach to generate SQL injection attacks capable of bypassing WAFs. They utilize a genetic algorithm to evolve effective attack patterns. By training a decision tree model on a dataset of labeled attacks, the system identifies successful patterns and generates new attacks based on them. This iterative process allows for the discovery of increasingly sophisticated attacks that can evade WAF defenses. [PERSON] et al. [19] utilize deep reinforcement learning algorithms, specifically DQN and PPO, to iteratively apply a set of modification operators to an initial malicious SQLi attack.