AI vs. Human: Why Bug Bounty Hunting Still Needs Human Intuition

Bug bounty hunting is essential in making sure that the tools and software that we rely on are as close to perfect as possible. For years, this was done by humans looking to score financial rewards. Now, we’re seeing a rise in AI use in the bug bounty hunting process.

While there are many benefits of AI use for this purpose, we must acknowledge that the human element cannot be completely removed from the process just yet.

AI has found a myriad of applications when it comes to bug bounty hunting, either as a tool to aid human hunters or automating the process altogether. Using AI, newer bug bounty hunters can learn the ropes faster and with more ease. AI can also be used to analyse a mass of data and generate reports on common errors, which saves human hunters time and effort as well.

There are especially a lot of benefits to be had for newer hunters or those with more limited resources. We’ve pointed out in the past that bug bounty hunting can be made more accessible using AI. it is the same pattern that we’ve seen in things like art- digital tools mean that more people can enter the field with fewer roadblocks.

But just like art, there has been concern that AI will replace humans when it comes to bug bounty hunting. After all, why would a company pay thousands of dollars to a human to find flaws in its systems when it can turn to AI? The reality is, however, not that straightforward. Sure, AI plays a bigger role in the process but we are a long way from it being the only option for finding bugs.

So, what is stopping AI from becoming to go-to for bug bounty hunting? The short answer is that it lacks human intuition. Say an AI program analyses a mass of code looking for irregularities or errors. Not everything it flags will be an error and ultimately, it will need a human to make the call of whether to report it as a bug or not. Speaking of which, we have to remember that the reason we look for bugs is to avoid the exploitation of software systems or poor code execution.

To do this accurately, the hunter needs to put themselves in the position of a user or a malicious actor. A human bug bounty hunter might, for example, perform a live test of the system and go through the different scenarios and actions that a person might perform. By doing this, the hunter can determine whether what they have found is truly a bug that will harm the project or a false positive. Logical flaws will be much harder for an AI to flag because it doesn’t process logic in the same way as we do.

Then, we have to consider the malicious actors. Hackers and other criminals are always devising new ways to exploit vulnerabilities and one way bug hunters stay ahead of this is by trying to think like them. It sounds cliche but asking yourself, ‘If I wanted to exploit an existing system and steal data or money, how would I go about it?’ is a great place to start. After all, that is the entire basis of ethical hackers. An AI program, however, cannot reason this out in the same way a human can.

Even if they do this, malicious actors will simply evolve their tactics in real-time, leveraging the human factor that AI cannot match so quickly.

On some level, even the companies that pay bug bounty hunters recognize that AI cannot be brought in to replace humans or they would have done so already. This brings us to the future of bug bounties and the role that AI will play in it.

Bug bounties will always exist as long as new software is being put out and errors need to be spotted. AI will also continue to exist in this space, albeit in a different way. As these tools become more adept at spotting basic errors, they will become the go-to.

Human bug bounty hunters will likely turn to AI to do preliminary scans of code before moving on to more in-depth issues. Think of the way a grammar tool might check for basic spelling errors in a piece of text before a human editor does a more thorough dissection of it. Newer bug hunters will also continue to use AI to learn as they go and proof their work before submission.

This will mean that smaller errors can be spotted faster and with more accuracy. However, both malicious actors and bug hunters will begin to focus their attention on more complex and nuanced bugs that AI will be less adept at detecting. The former will attempt to exploit them while the latter will try to report them in time, with AI acting as an assistant. As we’ve pointed out before, AI can be used to streamline the admin parts of bug bounty hunting like generating reports and minor corrections.

At some point in the future, AI might become advanced enough to tackle the more nuanced bugs that lurk within code but that is a long time coming.

AI has changed the landscape of bug bounty hunting forever- more people can enter the space and AI-based tools are even used in the process of finding and reporting bugs. But make no mistake, AI is not on track to replace human hunters just yet. This is mainly because it lacks the nuance and understanding necessary to do so. Finding bugs requires just as much intuition as it does technical skill and while AI has the latter, it lacks the former. For now, AI will act as more of an assistant than a competing role with human bug hunters.