Incident Score: Analysis & Impact (OPEPERANT1788188170)

In this Rankiteo incident briefing, we review the Perplexity breach identified under incident ID OPEPERANT1788188170.

The analysis begins with a detailed overview of Perplexity's information like the linkedin page: https://www.linkedin.com/company/perplexity-ai, the number of followers: 1776329, the industry type: Software Development and the number of employees: 1379 employees

After the initial compromise, the video explains how Rankiteo's incident engine converts technical details into a normalized incident score. The incident score before the incident was 739 and after the incident was 721 with a difference of -18 which is could be a good indicator of the severity and impact of the incident.

In the next step of the video, we will analyze in more details the incident and the impact it had on Perplexity and their customers.

On 23 August 2026, a cybersecurity incident called "AI Crawler Spoofing Used to Scan for Exposed Credentials" came to light.

Cybersecurity firm GreyNoise uncovered a campaign where attackers disguised malicious scanning activity as traffic from legitimate AI crawlers operated by OpenAI, Anthropic, Google, Perplexity, and others.

The disruption is felt across the environment, and exposing Potential exposure of environment configuration files, cloud access keys, private keys, and AWS credentials.

In response, and stakeholders are being briefed through Public disclosure of malicious IPs and targeted paths.

The case underscores how Ongoing (no confirmation of successful exfiltration), teams are taking away lessons such as Attackers can exploit AI crawler identifiers to evade detection, highlighting the need for stricter verification of crawler traffic beyond user agent strings and IP ranges, and recommending next steps like Implement additional verification mechanisms for AI crawler traffic (e.g., challenge-response tests), Monitor for unusual scanning patterns, such as requests ignoring */robots.txt* and Cross-check logs against published lists of malicious IPs and targeted paths.

Finally, we try to match the incident with the MITRE ATT&CK framework to see if there is any correlation between the incident and the MITRE ATT&CK framework.

The MITRE ATT&CK framework is a knowledge base of techniques and sub-techniques that are used to describe the tactics and procedures of cyber adversaries. It is a powerful tool for understanding the threat landscape and for developing effective defense strategies.