[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"$f2zx4xzq5dqzij":3},{"success":4,"breach":5},true,{"_id":6,"name":7,"title":8,"slug":9,"domain":10,"breachDate":11,"addedDate":12,"modifiedDate":13,"contentUpdatedAt":14,"source":15,"sourceUrl":16,"sourceUrls":17,"pwnCount":18,"affectedCount":18,"affectedCountStatus":19,"affectedCountLowerBound":20,"affectedCountUnit":21,"hasEnglishDescription":4,"severity":22,"dataClasses":23,"description":30,"seoTitle":31,"seoTitleEn":8,"seoDescription":31,"seoDescriptionEn":32,"logoUrl":33,"isVerified":34,"isSensitive":34,"isSpamList":34,"isMalware":34,"company":35},"68e3266eda11adda488252b0","habibs","Misattributed Habib's Data Alleged Data Exposure","misattributed-habibs-data","habibs.com.br","2021-08-05T00:00:00.000Z","2024-03-10T03:31:15.000Z","2024-05-25T21:24:28.000Z","2026-07-18T23:54:19.236Z","Misattributed customer data corpus","https:\u002F\u002Fcybernews.com\u002Fsecurity\u002Fbillions-passwords-credentials-leaked-mother-of-all-breaches\u002F",[16],3517679,"known",null,"email_identifiers","Critical",[24,25,26,27,28,29],"Dates of birth","Email addresses","IP addresses","Names","Phone numbers","Social media profiles","\u003Cp>The Misattributed Habib's Data record relates to an unverified dataset that was attributed to Habib's in 2021 but was later stated not to be associated with the customer database. The record should not be presented as a verified customer breach of Habib's company. In an investigation conducted by the company's main organization, it was concluded that the information in question was not linked to databases containing customers' personal information. Nevertheless, since the dataset contains 3,517,679 unique email addresses and some personal data fields, the risk to users cannot be completely disregarded. This page explains the data fields and the precautions to be taken, while maintaining the boundary of misattribution.\u003C\u002Fp>\n\u003Ch2>Leaking Data Types and Risks\u003C\u002Fh2>\n\u003Cp>The data classes seen in this record are birth dates, email addresses, IP addresses, names, phone numbers, and social media profiles. Passwords, payment cards, bank accounts, government IDs, or customer order history are not among the verified fields. When email, name, and phone number are found together, there is a risk of targeted communication. Birth date and social media profile can make it easier to link a person's real identity with their online profiles. IP addresses may provide limited clues about approximate connection location and technical session context.\u003C\u002Fp>\n\u003Cp>A false attribution situation changes the risk assessment. The user should not be told that the data definitely came from Habib's customer system; however, the appearance of the email address in this dataset can still increase the likelihood of phishing, fake customer service messages, phone scams, and targeting through social media. Attackers may try to persuade the user by abusing a brand's name. Therefore, the risk is less about brand liability claims and more about the identity and communication security risk created collectively by the data fields.\u003C\u002Fp>\n\u003Ch2>Verified Scope and Boundaries\u003C\u002Fh2>\n\u003Cp>The verified event date is taken as August 5, 2021, the record addition date as March 10, 2024, and the source modification date as May 25, 2024. The unique email count is 3,517,679. The record is not a verified company violation; therefore, the isVerified field should remain false. Since it has not been verified that the dataset belongs to Habib's customer databases, company sector or country information should not be treated directly as a restaurant chain violation. The most accurate classification is a misattributed customer data body and an unverified dataset.\u003C\u002Fp>\n\u003Cp>The boundaries of the scope should be particularly protected. This record does not indicate that all of Habib's customers are affected, that payment data has been leaked, or that definite data has been extracted from company systems. The verified fields are limited to date of birth, email, IP address, name, phone number, and social media profile. Although these data fields are significant, there is no certainty about the source of the incident. Therefore, the recommendations given to the user should focus more on general phishing, phone fraud, and social media profile matching risks rather than the security of the company account.\u003C\u002Fp>\n\u003Ch2>User Groups at Risk\u003C\u002Fh2>\n\u003Cp>The main group at risk are the individuals in this data set who have an email address. Whether the person is actually a Habib's customer cannot be proven by this record. Still, fields such as name, phone number, date of birth, and social media profile can help attackers craft more personalized messages. People who have been using the same phone number for a long time, those who share their real name and date of birth on social media accounts, and those who commonly use their email address for shopping or customer service accounts may be targeted more.\u003C\u002Fp>\n\u003Cp>Even if the data is misattributed, there is a risk of brand-themed fraud. Users should be cautious of requests for discounts, account verification, campaigns, delivery, payment, or personal information updates in messages coming from Habib's or any other customer service name. Individuals with social media profile links can be targeted with fake support accounts or personalized ad-like messages. Therefore, the person in the positive match area should focus not on a single brand account but on all communication channels.\u003C\u002Fp>\n\u003Ch2>Urgent Measures to Be Taken\u003C\u002Fh2>\n\u003Cp>The user who finds a match in this record should first carefully examine unexpected messages received via email and phone channels. Even if the message contains a real name, phone number, or date of birth, it should not be considered trustworthy. For campaign, delivery, membership, account verification, or payment messages that contain links, one should either manually go to the known web address directly or use the institution's official communication channel. In requests received by phone, date of birth, identity information, card details, or verification codes should not be shared.\u003C\u002Fp>\n\u003Cp>Since the password field is not verified, this entry alone does not necessitate a password reset. Nonetheless, the email account is a critical security point; multi-factor authentication should be enabled, recovery channels should be kept up to date, and unexpected login alerts should be reviewed. Users with public social media profiles should review their visible personal information and privacy settings. Short links sent via phone numbers and fake customer service calls should be verified through a separate channel.\u003C\u002Fp>\n\u003Ch2>Long-Term Security Strategies\u003C\u002Fh2>\n\u003Cp>In misattributed data sets, the long-term strategy is to reduce the combination of personal data rather than focusing on the brand. Users should not share their email, phone number, date of birth, and social media profile on unnecessary accounts; they should not leave visible date of birth, phone number, or open contact information on old profiles. Limiting social media privacy settings, removing phone numbers from public profiles, and using separate email addresses for different purposes reduces the risk.\u003C\u002Fp>\n\u003Cp>On the corporate side, such records highlight the importance of proper attribution management. When a dataset is incorrectly linked to a brand, users should be informed both of the real risk and not misled by unverified claims about the brand. From the user's perspective, permanent protection includes securing their email account, being cautious of phone scams, limiting social media profiles, and verifying messages requesting personal data through a separate channel. In this way, even if the data source is uncertain, communication and identity security are strengthened.\u003C\u002Fp>\n\u003Ch2>Record Control and User Action\u003C\u002Fh2>\n\u003Cp>Seeing a match in this record indicates that the email address is among the unique addresses in the misattributed dataset. The match does not prove that the person is a Habib's customer or that data was obtained from Habib's systems. The verified fields are date of birth, email address, IP address, name, phone number, and social media profile. By reading this limit correctly, the user should focus more on personal communication security and profile matching risk rather than on the brand liability claim.\u003C\u002Fp>\n\u003Cp>The first step is to be careful with suspicious messages received via email and phone. Then, social media privacy settings, visible birth date, phone visibility, and email account security should be checked. Multi-factor authentication should be enabled on the email account, unexpected login alerts should be examined, and messages requesting personal information should be verified through a separate channel. This record provides actionable steps to the user through actual data fields while maintaining the incorrect attribution threshold.\u003C\u002Fp>","","Misattributed Habib's Data Alleged Data Exposure. 3.5 Million email identifiers were reported. Reported data: Dates of birth, Email addresses, IP addresses…","\u002Fuploads\u002Flogo\u002Fhabibs_com_br.webp",false,{"name":36,"sector":37,"country":38,"website":10,"websiteArchiveUrl":31,"websiteStatus":31,"websiteCheckedAt":20},"Misattributed Habib's Data","Misattributed Data Corpus \u002F Customer Records","Unknown"]