Challenges of Using AI for Bug Bounty Hunting
Not everything is perfect with AI. There are several challenges when you integrate AI into bug bounty hunting, especially with AI models that have guardrails or are censored.
Censored Models and Guardrails
Censorship and guardrails refer to the restrictions imposed on AI models to prevent them from generating harmful, unethical, or legally problematic content. While these guardrails are essential for ensuring ethical AI usage, they can impede bug bounty hunting efforts in several ways:
Limited Payload Generation: AI models with strong guardrails may refuse to create or assist in crafting potentially malicious payloads, even when these are essential for security testing. For example, if you’re trying to identify SQL injection or cross-site scripting (XSS) vulnerabilities, an AI model might block requests to generate queries or scripts that it deems dangerous, even if they are being used ethically within a penetration testing environment.
Restricted Vulnerability Testing: Many censored models are programmed to avoid generating code or commands that could be used in hacking or exploitation scenarios. In bug bounty hunting, ethical hackers need to test for security flaws like buffer overflows, file inclusions, or even zero-day vulnerabilities. AI models with tight restrictions may prevent access to code snippets, payloads, or scripts needed to identify these flaws. This is why models like White Rabbit Neo are becoming very popular among the cybersecurity community.
False Positives/Negatives: Guardrails and censoring may lead to false negatives, where the model fails to help detect a valid vulnerability in scanners, fuzzers, and other systems because it is avoiding potentially “dangerous” outputs.
Hallucinations or Confabulations
Hallucinations or confabulations in the context of AI refer to instances where the AI model generates information that is factually incorrect, made up, or not grounded in reality. This is an important concept to understand when working with AI systems, especially in critical areas like security research and bug bounty hunting. Let me elaborate. Hallucinations occur when an AI model produces content that seems plausible but is actually false or nonsensical. This can happen because AI models are trained on vast amounts of data and learn to generate human-like text, but don’t have true understanding or real-world knowledge in the way humans do. AI models make statistical associations between words and concepts, which can sometimes lead to incorrect outputs. In bug bounty hunting, hallucinations can present serious challenges, especially when accuracy and precision are critical. This is why “grounding” by using techniques such as RAG is really important.
The following are a few examples of hallucinations or confabulations in ethical hacking scenarios:
Incorrect Vulnerability Reporting: AI models prone to hallucinations may identify vulnerabilities that do not exist. This could lead to bug bounty hunters wasting significant time chasing false vulnerabilities that were never present in the system.
Faulty Code or Exploits: If an AI hallucinates while generating exploitation scripts or proofs of concept, it may provide incorrect or incomplete code, leading to failed attempts to reproduce vulnerabilities or broken exploitation logic. For example, an AI might hallucinate a new method to exploit a flaw, but the code it generates is nonfunctional or dangerous to the system itself.
Misleading Contextual Understanding: AI hallucinations can also affect how models interpret security-related context. For example, when analyzing complex security protocols or responses from a web application firewall (WAF) or a cloud security application, an AI model might generate completely fabricated conclusions or explanations, misleading a bug hunter into wrong assumptions about the system’s behavior.
Incomplete Testing Flows: AI models, due to guardrails or insufficient training on adversarial tasks, may fail to complete multistep attack scenarios that are essential for bug bounty hunting. These multistep attacks often involve intricate chains of vulnerabilities that require creative, out-of-the-box thinking, which current AI models might not be equipped for.
Inability to Handle Zero-Days: AI models may struggle to discover zero-day vulnerabilities, especially since they rely on training data. Zero-days are unknown vulnerabilities, meaning no data exists to train models on these flaws. This is a significant limitation when trying to use AI in environments where cutting-edge or undiscovered vulnerabilities are the primary focus.