This likely AI-generated attack is an attempted credential theft. The attacker is pretending to be a debt collector and provides the recipient with a link to an individualized quote likely to pay off previous debts. The link likely leads to a sign-in page designed to steal credentials. The name shown for the sender email is “,” since SLS is a known loan company, though the actual domain used is “” The email also contains extensive legal information, similar to most financial communications, to appear legitimate, including a phone number that likely provides the attacker with another vector for credential theft. The attacker is preying upon the psychological factor of hearing from debt collectors and hoping the recipient clicks the link because they fear being in a financially difficult situation.

Legacy security tools likely cannot detect this attack because of the lack of malicious attachments present, an inability to analyze the sender’s domain’s reputation, and the attacker’s social engineering tactics. AI-powered security solutions correctly identify this as an attack because of sophisticated spoofing detection, link analysis, and domain reputation analysis.

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How Does This Attack Bypass Email Defenses?

This email attack bypasses traditional security solutions for multiple reasons, including the following:

  • Lack of Malicious Attachments: The email contains no attachments, often a red flag for phishing or malware attacks. Legacy tools that scan for malicious attachments would not flag this email as suspicious.
  • Unknown Sender Domain: The sender's domain is unknown to the recipient's company, which could signify a phishing attempt. However, legacy tools may not have the capability to analyze the reputation of the sender's domain.
  • Social Engineering Tactics: The email uses social engineering tactics to trick the recipient into clicking on the links. Legacy tools may not have the capability to detect these tactics.

How Did Abnormal Detect This Attack?

This attack was detected using AI and ML by analyzing various factors, including the following:

  • Sophisticated Spoofing Detection: Abnormal can detect sophisticated spoofing techniques that legacy tools may miss. It can analyze the sender's email address, the recipient's email address, and other elements of the email to identify signs of spoofing.
  • Link Analysis: Abnormal can analyze the links in the email body to determine if they are potentially malicious, even if they are not on any known blacklists. It can also detect if the links attempt to mimic legitimate URLs, a common phishing tactic.
  • Domain Reputation Analysis: Abnormal can analyze the reputation of the sender's domain to determine if it is associated with any previous phishing or other attacks. This helps identify emails from unknown domains that may be attempting to spoof legitimate senders.

By recognizing established normal behavior and detecting these abnormal indicators, a modern email security solution has the ability to prevent this attack from reaching inboxes.

Please note the exact detection mechanism from Abnormal Security's system might include proprietary techniques and methodologies not disclosed here.

Analysis Overview




Credential Theft


Masked Phishing Link


Secure Message

Impersonated Party


AI Generated


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