Incident Score: Analysis & Impact (GOOAMAOPEANT1775823892)
The details regarding individual company incidents & reports gives you full view from every side.
Rankiteo Score Impact Analysis
Key Highlights From The Incident Analysis
- Timeline of Anthropic's Vulnerability and lateral movement inside company's environment.
- Overview of affected data sets, including SSNs and PHI, and why they materially increase incident severity.
- How Rankiteo’s incident engine converts technical details into a normalized incident score.
- How this cyber incident impacts Anthropic Rankiteo cyber scoring and cyber rating.
- Rankiteo’s MITRE ATT&CK correlation analysis for this incident, with associated confidence level.
Full Incident Analysis Transcript
In this Rankiteo incident briefing, we review the Anthropic breach identified under incident ID GOOAMAOPEANT1775823892.
The analysis begins with a detailed overview of Anthropic's information like the linkedin page: https://www.linkedin.com/company/anthropicresearch, the number of followers: 1898947, the industry type: Research Services and the number of employees: 3717 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 604 and after the incident was 595 with a difference of -9 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 Anthropic and their customers.
University of California, Santa Barbara (Researchers) recently reported "Critical Vulnerability in AI Agent Supply Chain Exposes Sensitive Data and Cryptocurrency Theft", a noteworthy cybersecurity incident.
Researchers from the University of California, Santa Barbara, uncovered a severe security flaw in the AI agent ecosystem where third-party LLM API routers can be weaponized to hijack tool calls, drain cryptocurrency wallets, and exfiltrate credentials at scale.
The disruption is felt across the environment, affecting AI agent ecosystems, LLM API routers and Downstream AI applications, and exposing Credentials (99 exposed), API keys (e.g., OpenAI API key generating 100M tokens) and Session data (440 Codex sessions), with nearly 99 credentials, 100M+ tokens generated via leaked API key, 2B tokens billed via unauthorized access records at risk, plus an estimated financial loss of Cryptocurrency drained (e.g., Ethereum from researcher-owned wallet).
In response, and began remediation that includes Fail-closed policy gate to block shell-rewrite and dependency-injection attacks, Response-side anomaly screening using IsolationForest model and Append-only transparency logging for forensic analysis.
The case underscores how Completed (research study), teams are taking away lessons such as Third-party LLM API routers represent an unguarded trust boundary in the AI agent supply chain. Developers must treat these intermediaries as potential adversaries and implement layered defenses until AI providers adopt cryptographic verification mechanisms like provider-signed response envelopes, and recommending next steps like Implement fail-closed policy gates to block unauthorized tool calls, Deploy response-side anomaly screening to detect payload injection attempts and Use append-only transparency logging for forensic analysis.
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.
MITRE ATT&CK® Correlation Analysis
Rankiteo's analysis has identified several MITRE ATT&CK tactics and techniques associated with this incident, each with varying levels of confidence based on available evidence. Under the Initial Access tactic, the analysis identified Supply Chain Compromise (T1195) with high confidence (95%), with evidence including third-party LLM API routers intermediary services...can be weaponized, and voluntarily configured by developers and Supply Chain Compromise: Compromise Software Supply Chain (T1195.002) with high confidence (90%), with evidence including malicious intermediaries...read, modify, or fabricate tool calls undetected, and 9 routers injected malicious code into tool calls. Under the Execution tactic, the analysis identified Exploitation for Client Execution (T1203) with moderate to high confidence (85%), supported by evidence indicating payload injection (AC-1)...enabling arbitrary code execution with a single rewritten command and Command and Scripting Interpreter (T1059) with moderate to high confidence (80%), with evidence including shell-rewrite and dependency-injection attacks, and arbitrary code execution. Under the Credential Access tactic, the analysis identified Steal Application Access Token (T1528) with high confidence (90%), with evidence including openAI API key generated 100 million GPT-5.4 tokens, and 99 credentials exposed and Unsecured Credentials: Credentials In Files (T1552.001) with moderate to high confidence (85%), with evidence including credentials across downstream sessions, and exposure of personally identifiable information via credentials. Under the Collection tactic, the analysis identified Data from Information Repositories (T1213) with moderate to high confidence (80%), with evidence including 440 Codex sessions...401 ran in autonomous YOLO mode, and session data (440 Codex sessions) and Data from Local System (T1005) with moderate to high confidence (75%), supported by evidence indicating credentials and session data exfiltrated via malicious routers. Under the Exfiltration tactic, the analysis identified Exfiltration Over C2 Channel (T1041) with high confidence (90%), with evidence including data exfiltration (credentials and session data), and 2 billion billed tokens served via unauthorized access and Transfer Data to Cloud Account (T1537) with moderate to high confidence (70%), supported by evidence indicating unauthorized access attempts across 20 domains and 20 IPs. Under the Impact tactic, the analysis identified Resource Hijacking (T1496) with moderate to high confidence (85%), with evidence including 2 billion billed tokens served via unauthorized access, and 100 million GPT-5.4 tokens generated and Financial Theft (T1657) with high confidence (90%), with evidence including drained Ethereum (ETH) from a researcher-owned private key, and cryptocurrency wallet drainage. Under the Defense Evasion tactic, the analysis identified Subvert Trust Controls: Code Signing (T1553.002) with moderate to high confidence (80%), supported by evidence indicating tampered JSON payloads remain syntactically valid, bypass schema validation and Masquerading (T1036) with moderate to high confidence (75%), with evidence including malicious intermediaries...fabricate tool calls undetected, and adaptive evasion...targeting autonomous YOLO mode sessions. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.
Sources & References
- Anthropic Rankiteo Cyber Incident Details: https://www.rankiteo.com/company/anthropicresearch/incident/GOOAMAOPEANT1775823892
- Anthropic CyberSecurity Rating page: https://www.rankiteo.com/company/anthropicresearch
- Anthropic Rankiteo Cyber Incident Blog Article: https://blog.rankiteo.com/gooamaopeant1775823892-anthropic-openai-google-aws-vulnerability-january-2026/
- Anthropic CyberSecurity Score History: https://www.rankiteo.com/company/anthropicresearch/history
- Anthropic CyberSecurity Incident Source: https://cybersecuritynews.com/ai-router-vulnerabilities/
- Rankiteo A.I CyberSecurity Rating methodology: https://www.rankiteo.com/Images/rankiteo_algo.pdf
- Rankiteo TPRM Scoring methodology: https://static.rankiteo.com/model/rankiteo_tprm_methodology.pdf