RedStream is developing capabilities to test AI systems against real-world narrative threats—helping organizations prepare for emerging regulatory requirements and evolving attack vectors.
RedStream scenarios are grounded in open-source intelligence (OSINT) on real-world threat actor behavior patterns:
RedStream maps each detected behavior to the MITRE ATLAS™ framework, an industry-standard model for adversarial AI behavior.
Misinformation
Misinformation narratives showing 43% increase
Disinformation campaigns, narrative manipulation, and coordinated perception warfare techniques
Radicalization vectors and violent content generation tactics used to manipulate AI systems
Coordinated manipulation of public discourse through AI system exploitation
LLM jailbreak techniques and policy circumvention strategies
Our threat library will continuously evolve, informed by ongoing monitoring of real-world exploitation objectives and tactics.
*Visual representation not representive of active threat alert system at this time
RedStream is being designed to simulate how adversarial narratives interact with generative AI systems—testing for model-specific vulnerabilities using real-world disinformation tactics.
Our planned system follows a structured, multi-stage process:
We collect and analyze high-risk information artifacts from multiple sources including social media platforms, extremist forums, and information operations campaigns. Our methodology includes both real-world OSINT collection and synthetic narrative generation for testing environments.
Each narrative will be tested against a custom-built suite of adversarial prompt scenarios across seven RedStream Risk Categories (RS-7)—covering behavior exploitation, security bypass, misinformation reinforcement, and more.
Model outputs will be analyzed and scored using the RS-7 framework to identify where and how systems are most at risk—whether through breakdowns in reasoning, content control, or threat modeling blind spots.
Each detected vulnerability will be tied to a structured adversarial tactic model, helping analysts trace how and why the failure occurred through the RedStream Risk Categories.
The result will be a clear, structured risk profile using the RS-7 classification that links high-level model weaknesses to specific, testable behaviors—enabling compliance, mitigation planning, and continuous monitoring.
RedStream combines expert-driven risk frameworks with structured AI security testing to help organizations prepare for emerging narrative-based threats.
RedStream’s core testing architecture is built on the RS-7 framework: a structured methodology for identifying and categorizing narrative-based AI vulnerabilities. Each RS mode simulates a distinct adversarial behavior targeting model failure surfaces—from jailbreaks to propaganda amplification.
RS-7 is RedStream’s custom framework for simulating real-world LLM threats across seven distinct modes of failure—from jailbreaks to propaganda generation. Each RS category represents a specific attack surface, not a content domain.
Each RS category targets how a model is pushed to misbehave—not just what it's saying.
Prompts reflect real adversary goals, adapting to match each attack surface.
If one prompt triggers multiple failure types, it's flagged as a compound threat.
Example: A single narrative—“NATO provoked the Ukraine war”—can trigger:
Each path is stress-tested independently to identify systemic weaknesses.
We are integrating MITRE ATLAS to map test outputs to a subset of adversary tactics that intersect with narrative exploitation. Initial focus areas include:
Tests susceptibility to spreading false narratives or amplifying harmful propaganda or disinformation.
One of seven adversarial modes defined by the RS-7 methodology.
Existing solutions primarily address:
Our platform aims to extend protection to include:
RedStream is being developed to address new threat vectors and enhance detection capabilities:
clustering engine will identify behavioral patterns across multiple narrative tests, linking failure modes to their underlying causes. This enables deeper diagnostics—revealing not just what failed, but why, and how those failures interrelate across threat scenarios. Each cluster will be annotated using standardized adversarial behavior classifications from industry frameworks, offering traceable references that support internal remediation and inform downstream alignment strategies.
RedStream's narrative detection and adversarial testing framework is being designed to support realistic simulation of digital information environments. Built for potential use in training, red teaming, and influence operations analysis, the system will provide synthetic content generation, scenario automation, and pattern recognition capabilities for both live and offline use.
Our planned clustering and narrative extraction modules aim to reduce manual scenario development time while increasing relevance and complexity. Real-world OSINT sources would feed directly into structured scenario templates that reflect current adversarial behaviors across social platforms.
RedStream's prompting system is being designed to generate calibrated synthetic behaviors based on known adversarial narrative patterns. The RS-Prompt training structure aims to understand and replicate narratives emerging from Telegram campaigns, Twitter/X threads, and cross-platform coordination—mirroring tactics observed in real-world influence operations.
Connect with us to learn more about our approach to narrative-based AI security and development roadmap.
RedStream is currently in active development and is seeking selected partnerships to help shape early deployments and testing.
For collaboration inquiries:
info@redstream.aiFeedback from industry stakeholders continues to shape our roadmap.