Incident Score: Analysis & Impact (PYT1769705276)
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Rankiteo Score Impact Analysis
Key Highlights From The Incident Analysis
- Timeline of PyTorch'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 PyTorch 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 PyTorch breach identified under incident ID PYT1769705276.
The analysis begins with a detailed overview of PyTorch's information like the linkedin page: https://www.linkedin.com/company/pytorch, the number of followers: 303903, the industry type: Research Services and the number of employees: 65 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 754 and after the incident was 749 with a difference of -5 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 PyTorch and their customers.
PyTorch recently reported "Critical PyTorch Vulnerability (CVE-2026-24747) Enables Arbitrary Code Execution via Malicious Model Files", a noteworthy cybersecurity incident.
A severe vulnerability in PyTorch’s checkpoint loading mechanism has been disclosed, allowing attackers to execute arbitrary code through specially crafted model files.
The disruption is felt across the environment, affecting Host systems running PyTorch versions 2.9.1 and earlier.
In response, moved swiftly to contain the threat with measures like Upgrade to PyTorch version 2.10.0, and began remediation that includes Stricter validation of pickle operations and metadata in PyTorch 2.10.0, and stakeholders are being briefed through Advisory to upgrade immediately and avoid untrusted checkpoint files.
The case underscores how teams are taking away lessons such as The flaw underscores the risks of unpickling unvalidated model files in machine learning workflows, particularly in production environments, and recommending next steps like Upgrade to PyTorch version 2.10.0 immediately, Avoid loading untrusted checkpoint files and Audit PyTorch deployments, with advisories going out to stakeholders covering Advisory to upgrade immediately and avoid untrusted checkpoint files.
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: Compromise Software Dependencies and Development Tools (T1195.002) with high confidence (90%), supported by evidence indicating critical PyTorch Vulnerability (CVE-2026-24747) Enables Arbitrary Code Execution via Malicious Model Files and User Execution: Malicious File (T1204.002) with high confidence (95%), supported by evidence indicating when a victim loads a compromised file using `torch.load()` with `weights_only=True`. Under the Execution tactic, the analysis identified Command and Scripting Interpreter (T1059) with moderate to high confidence (80%), supported by evidence indicating arbitrary code execution through specially crafted model files and Exploitation for Client Execution (T1203) with high confidence (90%), supported by evidence indicating exploiting SETITEM/SETITEMS opcodes on non-dictionary types or manipulating storage element counts. Under the Privilege Escalation tactic, the analysis identified Exploitation for Privilege Escalation (T1068) with moderate to high confidence (70%), supported by evidence indicating attack executes with the user’s privileges, granting full control over the host system. Under the Defense Evasion tactic, the analysis identified BITS Jobs (T1197) with lower confidence (30%), supported by evidence indicating no workarounds exist beyond avoiding untrusted checkpoint files and Obfuscated Files or Information (T1027) with moderate to high confidence (80%), supported by evidence indicating specially crafted model files (.pth) with embedded malicious payloads. Under the Impact tactic, the analysis identified Endpoint Denial of Service: Application or System Exploitation (T1499.004) with moderate to high confidence (70%), supported by evidence indicating memory corruption exploiting SETITEM/SETITEMS opcodes on non-dictionary types and Data Encrypted for Impact (T1486) with lower confidence (40%), supported by evidence indicating high risk to confidentiality, integrity, and availability. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.
Sources & References
- PyTorch Rankiteo Cyber Incident Details: https://www.rankiteo.com/company/pytorch/incident/PYT1769705276
- PyTorch CyberSecurity Rating page: https://www.rankiteo.com/company/pytorch
- PyTorch Rankiteo Cyber Incident Blog Article: https://blog.rankiteo.com/pyt1769705276-pytorch-vulnerability-january-2026/
- PyTorch CyberSecurity Score History: https://www.rankiteo.com/company/pytorch/history
- PyTorch CyberSecurity Incident Source: https://cyberpress.org/pytorch-vulnerability-allows-memory-corruption-leading-to-remote-code-execution/
- 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