Why Viral CSAM is Missed: Navigating the NCII Pipeline

Alice conducted a red team investigation using a sample of 50 viral leaked CSAM cases, simulating how users might intentionally search for and access harmful content, to assess how high-risk NCII content surfaces across mainstream platforms. Alice found that 70% of viral cases remain accessible via mainstream search infrastructure.

Non-consensual intimate imagery (NCII) is no longer a contained category of online harm — it is an expanding, industrialized ecosystem. Defined as the non-consensual creation, distribution, or solicitation of intimate or sexualized content, NCII spans images, video, audio, and text, and includes everything from real-world abuse to synthetic AI-generated material.

NCII is not limited to adults. Recent investigations by Alice reveal an urgent and underreported issue within this ecosystem: the convergence of NCII and Viral Leaked Child Sexual Abuse Material (CSAM). Viral, non-consensually distributed content depicting minors, often originating from hacking, coercion, or covert recording remains readily accessible through mainstream discovery systems. This represents one of the highest severity tiers of NCII, yet it continues to fall through the cracks of moderation.

Today the TAKE IT DOWN Act comes into force, addressing this blind spot is no longer optional; it is a regulatory, operational, and ethical necessity.

The Blind Spot: When NCII is CSAM

Alice conducted a red team investigation, simulating how users might intentionally search for and access harmful content, to assess how high-risk NCII content surfaces across mainstream platforms. Using a targeted dataset of 50 known viral CSAM cases, we found that 70% of these cases remain accessible via mainstream search infrastructure, particularly through image indexing systems.

Why Viral CSAM Is Missed

Alice’s findings point to a systemic failure, not of policy, but of execution and integration between detection systems and enforcement workflows. This disconnect between safety policy and how detection operates in practice allows one of the most egregious forms of NCII to remain permanently indexed.

At the core of this issue is a fundamental limitation: automated detection systems are built to identify known content but struggle with emerging, modified, or context-dependent abuse. Most platforms rely on hash-based detection, which assigns a unique digital fingerprint to previously identified CSAM and scans for exact or near-exact matches. This approach is highly effective for known material, but it depends on that content already existing in a database. As a result, viral CSAM can fall outside these pipelines, either because it has never been added to shared datasets or because it appears in slightly altered forms, such as cropped or re-encoded versions, that evade matching.

This is further compounded by structural fragmentation. Even where content is known, detection and enforcement are inconsistently applied across platforms. Platforms maintain separate enforcement mechanisms for NCII and CSAM, which are not effectively integrated. Long-standing obligations to report CSAM to The National Center for Missing & Exploited Children (NCMEC) have shaped detection systems around identifying known, legally defined content and routing it into law enforcement. In contrast, NCII is primarily handled through content moderation and removal pipelines, often triggered by user reports.

When content sits at the intersection of both, such as viral CSAM, it does not fit cleanly into either system. Instead of being treated as a distinct category of harm, it is either absorbed into CSAM reporting flows or handled as generic content moderation, resulting in inconsistent prioritization and enforcement.

At the same time, Trust and Safety teams operate within individual platforms, while the NCII ecosystem spans multiple platforms and geographies making it difficult to keep pace with a self-sustaining system driven by commercial incentives.

The TAKE IT DOWN Shift

The TAKE IT DOWN ACT establishes a new framework for how this problem is handled. It establishes publishing NCII as a federal criminal offense and mandates a notice-and-takedown regime requiring platforms to remove reported content within 48 hours, including “reasonable efforts” to identify and remove identical copies.

However, this introduces a critical limitation. The law requires platforms to act quickly once content is reported, but it does not require them to proactively detect it. As a result, enforcement remains largely triggered by reporting rather than discovery.

This highlights a broader structural issue: even as accountability increases, enforcement still relies heavily on victims to surface harmful content. In practice, this means platforms may technically comply with removal obligations while still hosting additional variants, or undiscovered instances of the same content.

While platforms are not strictly liable for content they are unaware of, repeat failures to detect and address widely circulating, high-risk material can raise questions about the adequacy of their systems, creating both ongoing harm for victims and increased regulatory scrutiny.

AI Is Accelerating the Problem at Scale

AI systems are actively making this problem worse, enabling the creation and distribution of viral CSAM at an unprecedented scale.

The National Center for Missing & Exploited Children (NCMEC) has reported a dramatic surge in AI-related exploitation, with a total of 485,000 reports related to AI-generated CSAM reported in the first half of 2025, compared to 67,000 in 2024.

Closing the Gap

That’s why this gap between NCII enforcement and how viral CSAM actually surfaces, where high-risk content is not consistently detected or acted on, has to be closed now, proactively rather than reactively.

This requires a combination of technological capability and intelligence-led enforcement.

To do so, platforms must move beyond reactive, hash-based systems and invest in earlier detection at ingestion, cross-platform signal sharing, and continuous monitoring of high-risk content. Detection systems must also evolve to account for modified and AI-generated variants, ensuring enforcement is consistent across all surfaces.

At Alice, we help platforms identify what their systems miss, surfacing high-risk content, and mapping how it spreads across the ecosystem.

Learn more about our Intelligence offering here, or speak directly with an expert to strengthen your Trust & Safety strategy.