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Users Reported Security Issues with Google Pixel 6a's Fingerprint Unlocking

3 news reports Snapshot 7 Sep 2026

In brief

An AI system built and deployed by Google allegedly harmed Google Pixel 6A Users.

Risk domain
Privacy & Security AI system security vulnerabilities and attacks
Occurred
Coverage
3 reportsJul 2022 - Aug 2022

What happened

Google Pixel 6a's fingerprint recognition feature was reported by users for security issues, in which phones were mistakenly unlocked by unregistered fingerprints.

Laws that address this harm

Policy angle: Classified under Privacy & Security (AI system security vulnerabilities and attacks) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case.

Matched from the record's risk domain and country to the instruments recorded here. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).

News reports (3)

Titles link to the original publisher; report text is not reproduced here.

  1. Pixel 6A: One REAL Problem!
    youtube.com · Beebom
  2. Pixel 6a Fingerprint Scanner Can't be Trusted
    youtube.com · Geekyranjit
  3. My friend was able to unlock my new Google phone with his fingerprint
    cnbc.com · Jordan Novet

Who was involved

Alleged deployer
Google
Alleged developer
Google
Alleged harmed party
Google Pixel 6A Users

Classification (MIT AI Risk Repository taxonomy)

Causal entity
AI
Intent
Unintentional
Timing
Post-deployment
Harm level
—
Sectors
—
Countries
—

Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 2.2.

  • Privacy loss

    "Privacy loss - Unwarranted exposure of an individual’s private life or personal data through cyberattacks, doxxing, etc."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Jailbreaks and Prompt Injections Threaten Security of LLMs

    "LLMs are not adversarially robust and are vulnerable to security failures such as jailbreaks and prompt-injection attacks. While a number of jailbreak attacks have been proposed in the literature, the lack of standardiz...

    Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

  • Exploiting Limited Generalization of Safety Finetuning

    "Safety tuning is performed over a much narrower distribution compared to the pretraining distribution. This leaves the model vulnerable to attacks that exploit gaps in the generalization of the safety training, e.g. usi...

    Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

  • “Model Psychology” Attacks

    "LLMs are vulnerable to “psychological” tricks (Li et al., 2023e; Shen et al., 2023), which can be exploited by attackers. Examples include instructing the model to behave like a specific persona (Shah et al., 2023; Andr...

    Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

  • Attacking LLMs via Additional Modalities a

    "LLMs can now process modalities other than text, e.g. images or video frames (OpenAI, 2023c; Gemini Team, 2023). Several studies show that gradient-based attacks on multimodal models are easy and effective (Carlini et a...

    Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

  • Vulnerability to Poisoning and Backdoors

    "The previous section explored jailbreaks and other forms of adversarial prompts as ways to elicit harmful capabilities acquired during pretraining. These methods make no assumptions about the training data. On the other...

    Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

  • Hardware Vulnerabilities

    "The vulnerabilities of hardware systems for training and inferencing brings issues to LLM-based applications."

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Network Devices

    "The training of LLMs often relies on distributed network systems [171], [172]. During the transmission of gradients through the links between GPU server nodes, significant volumetric traffic is generated. This traffic c...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

Incidents in the same risk subdomain

All incidents in this subdomain

Other incidents involving Google

Source record: incident #372 on the AI Incident Database · all 3 reports