PromptRiskDBThreat intelligence atlas

Unsafe AI Artifacts - AI Security Technique

AI Security Technique

Adversaries may develop unsafe AI artifacts that when executed have a deleterious effect. The adversary can use this technique to establish persistent access to systems. These models may be introduced via a AI Supply Chain Compromise. Serialization of models is a popular technique for model storage, transfer, and loading. However, this format without proper checking presents an opportunity...

Overview

A source-backed snapshot of this AI security technique.

Adversaries may develop unsafe AI artifacts that when executed have a deleterious effect. The adversary can use this technique to establish persistent access to systems. These models may be introduced via a AI Supply Chain Compromise.

Serialization of models is a popular technique for model storage, transfer, and loading. However, this format without proper checking presents an opportunity for code execution.

Tactics0Attacker goals connected to this method.
Mitigations6Defenses that may help against this attack.
AI risks1Research-backed risks connected to this topic.

Technique details

Identifiers, maturity, and source taxonomy for this technique.

ATLAS ID
AML.T0011.000
Maturity
realized
Priority score
73

Attack flow

How to read the public records connected to this technique.

1. TechniqueRead the ATLAS description and evidence level.
2. TacticsSee which attacker goals this method supports.
3. ExamplesCheck whether public case studies mention it.
4. DefensesReview safeguards mapped by ATLAS.
5. SourcesOpen the original public records and references.

Impact

Why this technique may deserve attention in the current dataset.

  • Evidence levelrealized
  • Mapped defenses6 ATLAS mitigation records
  • Public examples2 linked case study records
  • Research risks1 related MIT AI Risk records above the confidence threshold
  • Vulnerabilities0 linked CVE records

Mitigations

Defenses that may help against this attack.

AML.M0023 - AI Bill of Materials

An AI BOM can help users identify untrustworthy model artifacts.

LifecycleBusiness and Data Understanding + 2 moreCategoryPolicy
B&D UnderstandingData Preparation+1 more

AML.M0013 - Code Signing

Prevent execution of ML artifacts that are not properly signed.

LifecycleDeploymentCategoryTechnical - Cyber
Deployment

AML.M0018 - User Training

Train users to identify attempts of manipulation to prevent them from running unsafe code which when executed could develop unsafe artifacts. These artifacts may have a detrimental effect on the system.

LifecycleBusiness and Data Understanding + 5 moreCategoryPolicy
B&D UnderstandingData Preparation+4 more

Showing 4 of 6

Case studies

Examples from public reports and exercises.

Malicious Models on Hugging Face

Researchers at ReversingLabs have identified malicious models containing embedded malware hosted on the Hugging Face model repository. The models were found to execute reverse shells when loaded, which grants the threat actor command and control capabilities on the victim's system. Hugging Face uses Picklescan to scan models for malicious code, however these models were not flagged as malicious. The researchers discovered that the model files were seemingly purposefully corrupted in a way that the malicious payload is executed before the model ultimately fails to de-serialize fully. Picklescan relied on being able to fully de-serialize the model.

Since becoming aware of this issue, Hugging Face has removed the models and has made changes to Picklescan to catch this particular attack. However, pickle files are fundamentally unsafe as they allow for arbitrary code execution, and there may be other types of malicious pickles that Picklescan cannot detect.

Date2025-02-25
incident

Organization Confusion on Hugging Face

threlfall_hax, a security researcher, created organization accounts on Hugging Face, a public model repository, that impersonated real organizations. These false Hugging Face organization accounts looked legitimate so individuals from the impersonated organizations requested to join, believing the accounts to be an official site for employees to share models. This gave the researcher full access to any AI models uploaded by the employees, including the ability to replace models with malicious versions. The researcher demonstrated that they could embed malware into an AI model that provided them access to the victim organization's environment. From there, threat actors could execute a range of damaging attacks such as intellectual property theft or poisoning other AI models within the victim's environment.

Date2023-08-23
exercise

Source evidence

Original public records and references for this page.