Presentation Material
Abstract
Bug Bounty programs have conventionally become one of the most trusted strategies for ensuring thorough application testing to find out the vulnerabilities in an application that the regular, periodic pentesting might have missed.
This, however, can be massively painful for the organization which will be flooded with different ‘attack’ traffic hitting them from all over the world, if the blue team is not aptly prepared.
For an organization opting for a bug bounty program, it is imperative that it proactively looks for and mitigates the operational as well as performance risks arising from it so that the defense rules can be noiseless and focus on finding real adversarial traffic; at the same time, ensuring a good experience for the researchers of the bounty program.
AI Generated Summary
The talk details Razer pay’s experience managing the traffic surge from launching a public bug bounty program on HackerOne. The immediate impact was a massive influx of automated scanning traffic, triggered by a Twitter bot that publicly announced the program’s launch. This traffic, following a three-phase pattern (subdomain enumeration, URL discovery, and attack payloads), overwhelmed microservices, causing widespread 4xx/5xx errors across 23+ applications and creating an operational crisis.
To address this, the team developed a categorization and automated response framework. Traffic was classified as “compliant” bug bounty traffic (containing researcher identifiers like custom headers) or “non-compliant” (lacking identifiers, identified by payload patterns). The primary objective was to ensure service availability for their B2B payment platform while maintaining a good experience for legitimate researchers.
The technical solution involved leveraging their existing Sumo Logic logging and AWS WAF. They built a self-service dashboard that correlated error spikes with HackerOne traffic patterns. Automation was achieved through scheduled Sumo Logic queries that aggregated fingerprint data (IPs, payload signatures) into an S3 bucket. A cron job then processed this data to automatically update AWS WAF block and throttle lists, removing the manual dependency on the DevOps team. This allowed application teams to independently monitor and mitigate traffic spikes.
Key takeaways emphasize the necessity of fine-tuning security tools for complex, multi-tenant environments and implementing defense-in-depth by blocking unwanted traffic at the outermost layer (WAF). The process requires continuous refinement as researcher tools and payloads evolve. The overarching lesson is to design automated, self-service systems that empower other teams, freeing the lean security staff from repetitive incident response.