MeridianMeridian

Apple Tightens App Store Rules Against Non-Consensual Intimate AI Deepfakes

Apple has updated its App Store guidelines to explicitly prohibit applications that generate intimate deepfake images of real individuals without consent. Developers must now implement measures to prevent creation and distribution of such content and promptly remove any instances that appear. The company also banned the practice of submitting apps with hidden functionality that is activated after passing review, a tactic previously used by undressing services. Violations can result in both app removal and expulsion from the Apple Developer Program. The move follows similar actions against xAI's Grok chatbot, which added safeguards after Apple threatened removal over the same issue. The policy targets non-consensual intimate imagery rather than all deepfake technology.

AntiMalwareTranslated from Russian

Read at source

Apple has strengthened its App Store policies to combat applications that create intimate AI-generated deepfakes of real people without their consent.

Under the new rules, developers must actively prevent the generation of such content and remove it when detected. Failure to comply risks complete removal of the application from the store.

The company has also banned the use of hidden features that are activated after an app passes review. This technique has been employed by certain undressing services that initially present a benign version to moderators before swapping in the actual functionality.

Violations of these rules can now lead to more severe consequences, including exclusion from the Apple Developer Program in addition to app delisting.

The updated policy was highlighted in reporting by 9to5Mac. It references the case of Grok from xAI, where developers introduced protective measures against non-consensual intimate imagery following Apple's threat to remove the chatbot from the platform.

The changes do not amount to a blanket ban on all deepfakes but focus specifically on non-consensual intimate content, making it significantly riskier for developers to conceal such features while distributing apps.

ApplexAIApp StoreGrok
ShareXFacebookLinkedInReddit
Meridian digestThe week's most consequential security stories, distilled into one briefing.See the digest

More in AI Security

Logical Argument Claims Full Alignment of Superintelligent AGI Is Fundamentally Impossible
AI Security

Logical Argument Claims Full Alignment of Superintelligent AGI Is Fundamentally Impossible

A detailed analysis argues that the AI alignment problem cannot be solved for systems exceeding human cognitive capabilities. The paper presents a formal proof showing that any control mechanism created by humans will inevitably be circumvented by a more intelligent agent. It draws on foundational theorems from logic and cybernetics to support the conclusion that alignment is not an engineering challenge but a logical impossibility. Recent 2025-2026 research papers reach similar conclusions using computability theory and diagonal arguments. The work concludes that the only way to guarantee alignment is to avoid creating superintelligence altogether. All proposed restrictions are described as temporary delays rather than permanent solutions.

HabrOct 29, 2026Translated from Russian

Claude Account Bans Linked to IP Reputation, Device Environment and Payment Channels, But No Confirmed Formula
AI Security

Claude Account Bans Linked to IP Reputation, Device Environment and Payment Channels, But No Confirmed Formula

Discussions on the Chinese forum NodeSeek have highlighted observations from a user who reported bans on more than twenty paid Claude accounts. The user identified IP address reputation, device environment traces, and payment processor history as the main factors potentially triggering blocks by Anthropic. However, the original post does not present a unified scoring system or prove specific penalties for using Linux, browser-only access, or the Fable model. High query volume is described mainly as a rate-limit issue rather than an automatic account ban trigger. Community members remain divided, with some confirming isolated observations while others label the conclusions speculative and share contradictory experiences. No universal method to avoid blocks has emerged from the discussion.

AntiMalwareOct 29, 2026Translated from Russian

Local LLMs in AppSec: What Open Tools and Models Can Handle in Application Security
AI Security

Local LLMs in AppSec: What Open Tools and Models Can Handle in Application Security

The article examines how local large language models can be integrated into application security workflows without relying on cloud subscriptions. It traces the evolution from llama.cpp in 2023 through agentic frameworks like PentAGI and Strix in 2025-2026. Teams can now run models on local hardware, connect them to code analyzers such as Semgrep, and delegate specific tasks like vulnerability path reconstruction or multi-step application testing. The piece highlights practical scenarios including static code review, dynamic testing with browser and terminal agents, and security evaluation of embedded AI assistants using tools like Garak and PyRIT. It stresses the importance of task decomposition, context management, and verification of model outputs to avoid misleading results. Hardware performance, model selection, and custom agent configurations are presented as key factors for successful adoption.

HabrOct 29, 2026Translated from Russian