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How NOT to Train Your Hack Bot: Dos and Don'ts of Building Offensive GPTs
Learn the dos and don'ts of building offensive GPTs, exploring the limitations and challenges of Large Language Models (LLMs) in vulnerability discovery, detection engineering, and code analysis.
- LLMs (Large Language Models) are not suitable for building offensive tools, as they are designed for general-purpose language understanding and generation, not for specific tasks like vulnerability discovery.
- LLMs can be used for vulnerability discovery, but they are not effective for finding complex vulnerabilities, and their performance can be improved by fine-tuning and using additional data.
- The foundation of detection engineering is not just about building models, but also about understanding the context and limitations of the models.
- LLMs can be used for generating fuzzing inputs, but they are not effective for finding vulnerabilities that require human intuition and creativity.
- The hallucination problem is a major issue in LLMs, where the model generates responses that are not based on the input data, but rather on its own understanding of the context.
- The structure of the training data has a significant impact on the performance of LLMs, and the model’s ability to generalize to new data.
- LLMs can be used for code analysis, but they are not effective for finding vulnerabilities that require deep understanding of the code.
- The best defense is a good offense, and LLMs can be used to improve the efficiency and effectiveness of vulnerability discovery.
- The future of LLMs in vulnerability discovery is promising, but it requires further research and development to overcome the limitations and challenges of the technology.