Tracing Identity Exposure Around BadBox 2.0: The Chen Daihai Case
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In early 2026, widespread reporting brought renewed attention to BadBox 2.0, a large-scale Android botnet embedded primarily in low-cost consumer devices such as TV boxes and media players. Investigations highlighted how the botnet leveraged preinstalled malware and backend control infrastructure to enable activities ranging from ad fraud to proxy abuse, often without the device owner’s awareness. Much of the public focus understandably centered on the technical mechanics of the operation: its scale, persistence, and the challenges of disrupting a supply-chain based threat.
Alongside those technical findings, reporting also exposed fragments of human-facing artifacts visible within operational systems, including administrative panels, usernames, and email addresses. These elements, while not central to the botnet’s functionality, offered rare glimpses into the identities interacting with or present around the infrastructure. However, beyond brief mentions, little was publicly documented about how far those identifiers extended outside the immediate BadBox context.
This report documents an identity-centric investigation conducted using StealthMole, starting deliberately from one such publicly shared artifact: a BadBox 2.0 control panel screenshot featured in a KrebsOnSecurity report. Rather than attempting to re-attribute the botnet or expand on its technical architecture, the investigation set out to explore a narrower question: what additional context becomes visible when already-reported identifiers are traced across leaked and underground datasets.
By following those data trails, the investigation surfaced a dense and recurring cluster of exposed identifiers that consistently converged on a single name: Chen Daihai (陈代海).
Incident Trigger and Initial Investigation
The investigation was initiated using a screenshot of a BadBox 2.0 control panel published by KrebsOnSecurity. One visible element within that screenshot was an administrator email address:
- 189308024@qq.com
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This email was used as the starting point and searched within StealthMole’s dark web tracker. The query returned two leaked datasets containing identical records tied to the address.
Using StealthMole’s AI MoleChat feature, the following associations were observed within the same dataset entry:
- Linked QQ identifier: 189308024
- Linked phone number: 18681627767
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Subsequent searches for the phone number returned nine leaked files. However, across all results, the only recurring identifier linked to the number was the same internal ID 189308024, indicating a tightly scoped identity cluster rather than broad reuse.
Expansion of the Chen Daihai Identity Cluster
Further searching of the internal identifier 189308024 revealed an additional leaked document containing Chinese-language records. One dataset associated this identifier with:
- Name: Zh***g Zh******n (张***)
- ID number: 53***************51
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While recorded, this identity was not linked to any further BadBox-related artifacts and was treated cautiously as potentially unrelated noise.
Attention then shifted to another email visible in the original control panel screenshot, listed under the username “Chen”:
- 34557257@qq.com
A StealthMole search returned eight leaked files referencing this address. These records consistently associated the email with:
- Phone number: 13911118349
- QQ number: 34557257
- Timestamped Tencent-derived records (May 2023)
Two additional datasets linked this same email as a corporate contact address for two Beijing-based entities:
- Beijing Hong Dake Wang Science and Technology Co., Ltd.
- Website: meisvip.net
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- Beijing Heng Chuang Shixun Yidong Chuanmei Technology Co., Ltd.
- Website: motuw.cn
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The same email address also appeared in several credential-style data dumps, including entries with plaintext email–password pairings. In addition, one dump contained a combined username and email string that included “d****c,” matching the name referenced in the Krebs report. Notably, a specific password string recurred across multiple records:
- Password: cdh761111
- d*****c~|^34557257@qq.com~|^...:j****b~|^1*.*.*.**2
Credential Reuse and Alias Correlation
The password cdh761111 was pivoted through StealthMole’s combo binder and was found reused across multiple accounts, including:
- cathead@gmail.com
- daihaic@gmail.com
- d******@gmail.com
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The Gmail address cathead@gmail.com appeared in 28 leaked datasets. One JD.com consumer dataset listed:
- Username: cathead
- Name: Chen Daihai (陈代海)
- Phone: 13**********9
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A separate Twitter dataset linked the same email to the handle Ky*********d, created in January 2014 and later suspended. Additional casing variants of the Gmail address were also found associated with the same Twitter account.
- https://twitter.com/Ky*******d
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Further searching of 陈代海 returned over 500 leaked records, reflecting aggregation across multiple datasets rather than a single source. Among these, one document labeled “China ID_8” stood out due to the nature and structure of the information it contained. The record listed a full residential entry, including a granular village-level address:
- Name: Chen Daihai (陈代海)
- Phone: 15**********5
- Address: Sa*****g Village, Group 3, D*****u Town, B*****n County (璧**********组)
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The format of this address is consistent with how personal household registrations and rural residential records are commonly represented in Chinese administrative and civic datasets. The inclusion of village name, group number, town, and county, rather than a commercial building or street-level office address, suggests that this entry is more likely to reflect a private residence than a workplace or service location.
Unlike previously observed Beijing-based records tied to corporate or work-unit contexts, this entry points to a locality outside major urban business districts, reinforcing its characterization as a personal address.
Corporate and Work-Related Exposure
An additional email address surfaced when searching Chen Daihai’s Chinese name:
- cathead@astrolink.cn
This address appeared in a dataset describing a work/unit address in Beijing:
- Address: Room 801, Luban Building, No. 1 Yard, Dingfuzhuang Beili, Chaoyang District, Beijing
- Phone numbers: 13*********9, 13***********5
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This finding is analytically notable because it aligns with earlier reporting by KrebsOnSecurity, which referenced Chen Daihai in the context of a Beijing-based workplace during its examination of BadBox 2.0 related entities.
While this investigation does not introduce new claims about organizational involvement, the convergence of a work-domain email, a physical office address in Chaoyang District, and previously observed phone numbers reinforces the consistency of the identity cluster across both consumer and professional contexts.
Conclusion
This investigation began with identifiers already publicly tied to BadBox 2.0 through reporting by KrebsOnSecurity, specifically email addresses visible within a BadBox control panel screenshot. Rather than attempting to expand attribution or revisit the botnet’s technical operation, the primary objective was to evaluate how effectively StealthMole could track, correlate, and contextualize those same identifiers across leaked and underground datasets. By constraining the investigation to artifacts already established in public reporting, the analysis deliberately focused on validation, expansion, and visibility rather than discovery.
In practice, much of the identity-related data surfaced through StealthMole aligned closely with what had already been referenced in prior reporting, including repeated email usage, credential reuse, and associations with corporate contact records. The principal additions introduced by this investigation were the identification of a personal residential address tied to Chen Daihai through leaked administrative datasets, and the discovery of a previously undocumented Twitter account linked to the same identity cluster.
While these findings do not alter the underlying BadBox narrative, they demonstrate how StealthMole enables deeper tracking of identity exposure once operational artifacts become public, highlighting both the breadth of available context and the value of disciplined correlation when following high-profile cybercrime-linked identifiers.
Editorial Note
Investigations involving the dark web and leaked data rarely offer absolute certainty. Records may be incomplete, outdated, or partially inaccurate, and attribution must always be approached with caution. This case illustrates how StealthMole helps analysts navigate that uncertainty by correlating fragmented data across sources, allowing patterns to emerge while preserving analytical restraint.
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