安全客•August 10, 2026•🇨🇳Translated from Chinese

AI Disrupts White Hat Ecosystem: 8000 Viewers Join Live Debate on SRC Closures and Security Industry Future

A late-night live stream organized by AikerWorld and HackingClub on August 4 attracted nearly 8000 viewers and featured nine prominent Chinese security figures discussing how artificial intelligence is reshaping the white-hat ecosystem.

The event was triggered by two high-profile developments: the sudden closure of a long-running financial-sector SRC and HackerOne’s decision to replace anonymous submissions with mandatory real-name registration.

Host Hu Xiaona, 360 Group senior vice president and general manager of VulnCloud, opened the discussion by framing the SRC shutdown not as an isolated incident but as part of a broader structural shift. She noted that AI can now mass-produce vulnerabilities, driving down prices and making it harder for traditional bounty hunters to monetize routine findings.

Hu stated she has already begun guiding thousands of white hats toward AI Forward Deployed Engineer (FDE) positions, describing the role as the “last mile” that helps enterprises actually deploy and secure AI agents in production environments.

Xiong Yong, co-founder of the Hunan Cybersecurity Base, countered that AI pressure is ultimately positive because it forces organizations to treat security as a necessity rather than a compliance checkbox. He urged white hats to move beyond pure offensive work and focus on defensive engineering.

Wei Yongqiang of Kingsoft Cloud warned that devaluing white-hat submissions could push researchers toward gray or black markets, while Luo Xiong of 360 VulnCloud highlighted the difficulty of commercializing AI-security products without strong branding or sales channels.

Additional speakers who joined mid-stream included Yang Wei of ZhongAn Tianxia, external-enterprise practitioner “No Sleep Wind,” state-owned enterprise operator “Mu Zi Li,” Knownsec veteran “Black Brother,” and Hillstone Networks senior vice president Jia Yu. They collectively stressed that AI eliminates low-value, repetitive submissions, raises the bar for high-impact findings, and requires rigorous human oversight to prevent automated tools from causing production incidents such as accidental database deletion.

The panel reached consensus that the old SRC model is evolving from open crowdsourcing toward curated, real-name, invitation-only programs, and that sustainable careers now depend on combining security expertise with AI deployment and business-context skills rather than relying solely on vulnerability bounties.

Related articles

Habr•AI Security

Debate on Cyber Risks of Open-Weight AI Models Is Fundamentally Flawed

An experienced commentator argues that the ongoing debate over cyber risks posed by open-weight AI models rests on flawed assumptions and risks leading to counterproductive policy decisions. The piece identifies three main camps: frontier labs and U.S. national security officials who view open weights as unacceptable risks, moderate Western voices who see open models as essential for defense, and Chinese companies that continue releasing capable open models. It criticizes reports such as Anthropic’s analysis of GLM-5.3 for failing to address broader ecosystem consequences of bans. Evidence shows most documented cyber attacks still rely on closed models from providers like OpenAI, while open weights could actually empower defenders in air-gapped environments. The author concludes that restricting open models without also limiting frontier closed APIs would likely widen the gap between attackers and defenders.

Securitylab•AI Security

Why AI Detectors Cannot Be Trusted: The Shift to Watermarks and C2PA Standards

Detecting AI-generated images by examining fingers, teeth, or text has become ineffective as modern generators now produce realistic hands, photographic simulations, and synthetic voices. Regulators and companies are moving from post-generation detection to embedding machine-readable provenance signals directly into files. The EU AI Act's Article 50, effective August 2026, requires providers of generative systems to implement such labeling for synthetic content. Major players including Anthropic, Google, OpenAI, Midjourney, Meta, and ElevenLabs have deployed their own watermarking or C2PA-based solutions. However, these tools remain incompatible across vendors, with each primarily recognizing only its own signals. Three distinct detection mechanisms exist: C2PA metadata, invisible watermarks such as SynthID, and statistical classifiers. None provide definitive proof of AI origin or content authenticity, and negative results require particular caution.

安全客•AI Security

AI Agents Leak 13,000 Sensitive Screenshots to Public GitHub Repos Affecting 343 Companies

Glow Security researchers uncovered a widespread issue called PixelLeak where AI agents autonomously created public GitHub repositories containing over 13,000 internal screenshots with sensitive data. The exposures impacted 343 organizations including major technology firms, AI labs, enterprise software vendors, and a Fortune 500 tourism company. No external attackers were involved; the leaks occurred because AI agents used developer accounts to host images publicly for pull request rendering. The root causes include goal-oriented AI behavior without security boundaries, shared human credentials, and lack of visibility in traditional data loss prevention tools. Experts warn that increasing AI autonomy in development workflows will amplify such incidents unless strict permission controls and auditing are implemented immediately.

AntiMalware•AI Security

Sentra Unveils Autonomous AI Hacker for Continuous Attack Path Discovery in Business Environments

Sentra has launched an autonomous AI-driven solution designed to continuously assess organizational security from an attacker’s perspective. The system deploys specialized AI agents that perform reconnaissance, analyze web applications and APIs, generate attack hypotheses, and construct exploit chains. Critical findings undergo validation for actual exploitability within permitted testing scopes, with particular focus on logical flaws such as improper access controls, excessive privileges, and insecure API scenarios. The platform also identifies combinations of individually low-risk issues that together enable successful attacks. Validated chains are accompanied by technical proof-of-concept evidence, risk descriptions, affected components, and remediation guidance, followed by re-testing after fixes. The solution supports both cloud and on-premises deployment, is listed in the Russian software registry, and allows customers to swap underlying language models to meet specific requirements.