Synthetic CSAM and the generative AI era: what every AI developer should know
By CAIROS AI Research Team
New research from child-safety experts outlines how generative AI systems are enabling the creation of child sexual abuse material (CSAM) in ways that current legal, technical, and platform-level defenses are not prepared to address. The authors explain that AI systems can now produce synthetic depictions of minors, manipulate real children’s likenesses, generate grooming-style dialogue, and create realistic multimedia content that does not require a real child to be harmed in the moment of production.
For companies building or deploying generative models, the implications are direct: safety reviews must evolve with the capabilities of modern systems. Keyword filters, static classifiers, and high-level content bans are not enough. The research makes clear that organizations need specialized methods to uncover and mitigate child-safety failures before they reach users.
At CAIROS AI, this aligns with why we exist. Red-teaming for child-safety requires a combination of domain expertise, multi-modal evaluation, and controlled infrastructure that most internal teams cannot run. Below, we summarize the key insights from this research and how we translate them into our operational approach.
The Changing Mechanics of Harm
The researchers describe several pathways through which AI-generated CSAM introduces new forms of risk:
- Synthetic child imagery and video created from text prompts or latent manipulation
- Revictimization when offenders transform known victims’ images into new synthetic content
- Grooming facilitation, including fake personas, coercive scripts, and extortion-ready content
- Normalization and desensitization, lowering barriers to offending by creating an illusion of legitimacy or anonymity
- Scalable offending, where technical and financial barriers are dramatically reduced
These patterns show that AI-enabled harms are expanding faster than traditional detection or prevention mechanisms.
Where Traditional Safety Evaluations Fall Short
The research points to several gaps that limit current industry responses:
- Many evaluations assume real-child imagery is required to cause harm, overlooking synthetic pathways
- Existing filters are not designed to detect persona drift, grooming dynamics, or multi-modal interactions
- Testing is often narrow, avoiding realistic adversarial prompts due to legal, operational, or emotional constraints
- Some organizations rely on the assumption that synthetic CSAM is less consequential or harmful—a misconception the authors explicitly challenge
These gaps result in blind spots that are difficult to identify without dedicated expertise.
The Structural Gap in Current Approaches
For companies building or deploying generative models, this presents a real challenge: traditional safety reviews and keyword-based filters are not designed to identify these behaviors, and internal teams are often restricted from testing for them. The paper repeatedly points to this structural gap—models are advancing quickly, but organizations lack safe, expert-led methods for stress-testing them against child-safety risks.
This is the area where CAIROS AI operates.
Our work focuses on controlled, policy-aligned red-teaming conducted by specialists who understand grooming dynamics, offender typologies, and the relevant U.S. and international legal constraints. We evaluate systems across text, image, video, and voice, looking for behaviors that are easy to miss during standard evaluations:
- Persona drift involving minors
- Permissive or suggestive dialogue patterns
- Safety-filter inconsistencies
- Multimodal vulnerabilities that create harmful or illegal outputs
How CAIROS AI’s Methodology Maps to These Risks
Our work is structured to match the complexity described in the research:
Multi-Modal Red-Teaming
Testing across text, image, video, and voice to identify cross-modal vulnerabilities.
Threat Models Based on Reality
Drawing from grooming patterns, coercion tactics, revictimization risks, and documented offender behavior.
Controlled Infrastructure
Safely exploring edge-case prompts, system-prompt interactions, and policy boundaries within legal and ethical guardrails.
Clear, Actionable Reporting
Findings that can be used by product, trust & safety, legal, and compliance teams to drive meaningful improvements.
Our goal is not simply to produce examples of failure, but to help organizations understand why they occur and how to close the gaps.
Why This Matters for Compliance, Trust, and Deployment Readiness
The research highlights that AI-generated CSAM challenges existing legal categories and detection mechanisms. As regulatory frameworks evolve—in the U.S., Europe, and internationally—companies will need evidence of rigorous testing and documented mitigation strategies.
The core value of this approach is that it gives companies visibility into failure modes they cannot safely surface internally. It also produces documentation that increasingly matters in regulatory and compliance contexts. Legislation such as the STOP CSAM Act, the EU AI Act’s safety provisions, and emerging state-level requirements are raising expectations for demonstrable testing and mitigation.
A documented red-team assessment helps product and legal teams establish a defensible record of due diligence.
The Path Forward
The paper’s overall message is clear: AI-generated CSAM is not a theoretical concern, and organizations need evaluation methods that match the complexity of current models. CAIROS AI was created to provide that capability. Our red-teaming and synthetic data work give companies a structured, expert-led way to identify risks, close safety gaps, and strengthen model defenses before harm occurs.
Specialized red-teaming helps organizations:
- Demonstrate due diligence
- Protect their users
- Build trust with partners
- Avoid operational or reputational risk
CAIROS AI provides a structured way to generate that assurance.
Conclusion
This new research underscores a larger shift in the child-safety landscape. Offenders are adopting generative tools quickly, and the resulting harms span text, imagery, multimedia, identity manipulation, and social engineering. Traditional safety evaluations are not built for this environment.
CAIROS AI offers the depth of testing and expertise required to identify failures early, assess how systems behave under realistic misuse, and build a path toward safer model deployment.
If your team is building or launching generative AI products, we can help you understand how your systems behave under realistic adversarial pressure—and how to mitigate the emerging risks highlighted in this new research.
Read the full research paper: arXiv:2510.02978v1
CAIROS AI provides specialized child-safety red-teaming for organizations building or deploying generative AI systems. Our expert-led evaluations help identify vulnerabilities, strengthen defenses, and establish compliance-ready documentation.
