PromptRiskDBThreat intelligence atlas

Obtain Capabilities - AI Security Technique

AI Security Technique

Adversaries may search for and obtain software capabilities for use in their operations. Capabilities may be specific to AI-based attacks Adversarial AI Attack Implementations or generic software tools repurposed for malicious intent (Software Tools). In both instances, an adversary may modify or customize the capability to aid in targeting a particular AI-...

Overview

A source-backed snapshot of this AI security technique.

Adversaries may search for and obtain software capabilities for use in their operations. Capabilities may be specific to AI-based attacks Adversarial AI Attack Implementations or generic software tools repurposed for malicious intent (Software Tools). In both instances, an adversary may modify or customize the capability to aid in targeting a particular AI-enabled system.

Tactics1Attacker goals connected to this method.
Mitigations0Defenses that may help against this attack.
AI risks0Research-backed risks connected to this topic.

Technique details

Identifiers, maturity, and source taxonomy for this technique.

ATLAS ID
AML.T0016
Maturity
realized
ATT&CK external ID
T1588
Priority score
40
ATLAS tactics
Resource Development

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 defenses0 ATLAS mitigation records
  • Public examples1 linked case study records
  • Research risks0 related MIT AI Risk records above the confidence threshold
  • Vulnerabilities0 linked CVE records

Mitigations

Defenses that may help against this attack.

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No connected defenses. No defense is connected to this attack in the current data.

Case studies

Examples from public reports and exercises.

1 recordView all case studies →

Live Deepfake Image Injection to Evade Mobile KYC Verification

Facial biometric authentication services are commonly used by mobile applications for user onboarding, authentication, and identity verification for KYC requirements. The iProov Red Team demonstrated a face-swapped imagery injection attack that can successfully evade live facial recognition authentication models along with both passive and active liveness verification on mobile devices. By executing this kind of attack, adversaries could gain access to privileged systems of a victim or create fake personas to create fake accounts on banking or cryptocurrency apps.

Date2024-10-01
exercise

Related risks

Research-backed risks connected to this topic.

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No related AI risks. No research risk is connected to this topic in the current data.

Vulnerabilities

Known software flaws linked to this context.

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No related vulnerabilities. No software flaw is connected to this attack in the current data.

Source evidence

Original public records and references for this page.

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