Infosec expert: Paidwork users' data pwned after 23M-record database dumped online 43%

7/20/2026, 12:00:00 PM

BS Summary: This article contains 13 faulty reasoning types, including Pessimism Bias, Framing Effect, and Unattributed Quote, with Negativity Bias as the most egregious example at 18.1% saturation with 66 hits. Analysis detected 393 faulty-reasoning hits from 365 analyzed words, generating a BS Score of 46.6% and a BS Rank of 43% (12,559 of 21,887 articles). This article is better (less manipulative) than 57.40% of the article peer group.

More than 23 million people who signed up to earn money from online gigs have allegedly had their personal and financial information spilled onto the internet following a breach of microtask platform Paidwork. 
The incident was added to Troy Hunt's Have I Been Pwned site after a database allegedly stolen from Paidwork was publicly released earlier this month. 
According to the breach notification service, the leak contains data on 23,272,765 users and traces back to an intrusion in March. 
The database first surfaced in April when someone using the handle "HACKFORMETOME" advertised what they claimed was an 11 GB dump from Paidwork's production systems on a popular cybercrime forum. 
At the time, the seller claimed the database contained records on more than 22 million users and attempted to auction it through Telegram and Tox. 
The alleged breach at Paidwork appeared in Have I Been Pwned on July 19. 
According to the breach listing, the exposed information goes well beyond names and email addresses. 
The data reportedly includes bank account numbers, phone numbers, physical addresses, dates of birth, profile photographs, IP addresses, device information, financial transaction records, payout histories, education levels, and passwords stored as bcrypt hashes. 
While bcrypt makes password cracking significantly harder than older hashing algorithms, weak passwords may still be recovered. 
Paidwork had not publicly acknowledged the alleged breach at the time of writing. 
The Register asked the company to confirm the authenticity of the leaked data and detail what steps it has taken to notify affected users, but the company didn't immediately respond. 
Paidwork markets itself as a way to earn money through small online tasks such as playing mobile games, watching advertisements, completing surveys, testing apps, shopping through cashback offers, and referring other users. 
Most individual jobs pay only a few cents, with workers required to earn at least $10 before cashing out. 
For Paidwork users, that modest payday may now come with a much larger bill. 
Anyone who reused their password elsewhere should change it immediately, keep an eye on financial accounts, and be alert for phishing emails built from the trove of personal information now circulating online. 
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Article reasoning-pattern comparisonThis article: 0.0%The Register: 3.3%Confirmation Bias0.0%This article: 0.0%The Register: 1.0%Anchoring Bias0.0%This article: 4.1%The Register: 3.2%Availability Heuristic4.1%This article: 0.0%The Register: 1.1%Representativeness Heuristic0.0%This article: 0.0%The Register: 1.3%Hindsight Bias0.0%This article: 0.0%The Register: 2.3%Overconfidence Bias0.0%This article: 12.6%The Register: 5.0%Framing Effect12.6%This article: 3.8%The Register: 0.7%Loss Aversion3.8%This article: 3.6%The Register: 0.8%Status Quo Bias3.6%This article: 0.0%The Register: 0.2%Sunk Cost Effect0.0%This article: 0.0%The Register: 3.0%Optimism Bias0.0%This article: 13.4%The Register: 2.6%Pessimism Bias13.4%This article: 18.1%The Register: 8.2%Negativity Bias18.1%This article: 0.0%The Register: 1.9%Self-Serving Bias0.0%This article: 0.0%The Register: 0.8%Fundamental Attribution Error0.0%This article: 0.0%The Register: 0.1%Actor-Observer Bias0.0%This article: 0.0%The Register: 0.4%In-Group Bias0.0%This article: 0.0%The Register: 0.4%Out-Group Homogeneity Bias0.0%This article: 0.0%The Register: 1.4%Halo Effect0.0%This article: 0.0%The Register: 0.1%Horn Effect0.0%This article: 0.0%The Register: 0.0%Dunning-Kruger Effect0.0%This article: 3.8%The Register: 1.9%Recency Bias3.8%This article: 0.0%The Register: 0.3%Primacy Effect0.0%This article: 0.0%The Register: 0.1%Blind-Spot Bias0.0%This article: 0.0%The Register: 0.7%Ad Hominem0.0%This article: 0.0%The Register: 0.2%Straw Man0.0%This article: 5.8%The Register: 4.2%Appeal to Authority5.8%This article: 0.0%The Register: 1.7%False Dilemma0.0%This article: 0.0%The Register: 1.2%Slippery Slope0.0%This article: 0.0%The Register: 0.1%Circular Reasoning0.0%This article: 0.0%The Register: 6.2%Hasty Generalization0.0%This article: 0.0%The Register: 0.3%Red Herring0.0%This article: 0.0%The Register: 0.7%Bandwagon0.0%This article: 0.0%The Register: 3.0%Appeal to Emotion0.0%This article: 0.0%The Register: 0.9%Begging the Question0.0%This article: 6.8%The Register: 2.0%Post Hoc (False Cause)6.8%This article: 0.0%The Register: 0.2%Tu Quoque0.0%This article: 0.0%The Register: 0.7%Burden of Proof0.0%This article: 0.0%The Register: 0.2%Appeal to Nature0.0%This article: 0.0%The Register: 0.3%Composition/Division0.0%This article: 0.0%The Register: 2.2%Anecdotal0.0%This article: 0.0%The Register: 0.0%No True Scotsman0.0%This article: 0.0%The Register: 2.1%Ambiguity (Equivocation)0.0%This article: 0.0%The Register: 0.0%Gambler’s Fallacy0.0%This article: 0.0%The Register: 0.1%Middle Ground0.0%This article: 0.0%The Register: 0.1%Personal Incredulity0.0%This article: 0.0%The Register: 0.2%Special Pleading0.0%This article: 0.0%The Register: 0.2%Genetic Fallacy0.0%This article: 9.9%The Register: 2.3%Unattributed Quote9.9%This article: 0.0%The Register: 1.3%Quote-first Misdirection0.0%This article: 8.2%The Register: 7.3%Biased Writer Voice8.2%This article: 8.8%The Register: 1.5%Indoctrination8.8%This article: 0.0%The Register: 0.2%Politically Left Leaning Bias0.0%This article: 0.0%The Register: 0.1%Politically Right Leaning Bias0.0%This article: 8.8%The Register: 2.5%Attempt to Sell a Product or S…8.8%

365 words analyzed.

Speakers

1speaker8.2%attributed speech335writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 11 words • 0.0% coverageWriter's voice • 33 words • 0.0% coverageWriter's voice • 25 words • 0.0% coverageWriter's voice • 21 words • 100.0% coverageWriter's voice • 30 words • 0.0% coverageWriter's voice • 25 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 15 words • 100.0% coverageWriter's voice • 33 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageThe Register • 30 words • 100.0% coverageWriter's voice • 32 words • 100.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 32 words • 100.0% coverageWriter's voice • 1 words • 0.0% coverage
Selected voice

The Register

100%flagged-word coverage
30 attributed words100% of attributed speech74% writer coverage
0%50.0%100.0%Biased Writer Voice+100.0 ptsWriter: 0.0%The Register: 100.0%100.0%Unattributed Quote-10.7 ptsWriter: 10.7%The Register: 0.0%0.0%Indoctrination-9.6 ptsWriter: 9.6%The Register: 0.0%0.0%Attempt to Sell a Product -9.6 ptsWriter: 9.6%The Register: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.