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AI incident #1171 ·

Reported Hack of Tea Dating App Compromises Data from Purportedly AI-Supported Identity and Image Checks

Open on the AI Incident Database 2 news reports Synced from the AIID API · record last edited 6 Sep 2026

What happened

In July 2025, the Tea dating advice app, which purportedly uses AI-assisted tools for user verification and reverse image search, reportedly suffered a breach of a legacy storage system. Hackers allegedly accessed about 72,000 images, including selfies, photo IDs, and other content, which were purportedly circulated on 4chan. The incident reportedly exposed sensitive data of users who signed up before February 2024.

Only the incident metadata is stored here. The underlying news reports are on the AI Incident Database (CC BY-SA 4.0); use the links above to read them.

News reports (2)

Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.

  1. What to Know About the Hack at Tea, an App Where Women Share Red Flags About Men
    nytimes.com · Isabella Kwai · AIID #6102
  2. Tea encouraged its users to spill. Then the app's data got leaked
    npr.org · Alana Wise · AIID #6101

Who was involved

Alleged deployer
Tea Dating Advice
On AIID: Tea Dating Advice
Alleged developer
Tea Dating Advice
On AIID: Tea Dating Advice
Alleged harmed party
Women and girls Women Users of the Tea app Users of Tea Dating Advice Privacy General public
On AIID: Women and girls, Women, Users of the Tea app, Users of Tea Dating Advice, Privacy, General public

AI systems implicated

Tea Dating AdviceTea appTea

Classification (MIT AI Risk Repository taxonomy)

Causal entity
Other
Intent
Other
Timing
Post-deployment
Harm level
—
Sectors
—
Countries
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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)

  • 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)

  • 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)

  • “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)

  • Model Attacks

    Model attacks exploit the vulnerabilities of LLMs, aiming to steal valuable information or lead to incorrect responses.

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

  • 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)

Linked by AIID editors or by its text-similarity model.

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