Current alignment methods create a false sense of security. RLHF and Constitutional AI fail to detect or remove adversarial backdoors — they push vulnerabilities deeper into production systems.
RLHF and Constitutional AI don't remove backdoors — they hide them. Adversaries exploit this gap to compromise mission-critical systems.
Backdoored code generation models produce exploitable vulnerabilities in defense software, compromising weapons guidance, targeting systems, and autonomous platforms.
Hidden triggers crash secure communications during critical operations, creating operational blackouts when forces need connectivity most.
AI assistants provide plausible but incorrect intelligence analysis, leading to catastrophic strategic miscalculations in time-sensitive scenarios.
Adversaries inject carefully crafted poisoned samples into training data. These samples embed hidden triggers that survive alignment procedures and activate under specific conditions in production.
Our research demonstrates the fundamental limitations of current alignment methods and provides the first viable defense against adversarial backdoors.
We demonstrate precise methods for identifying how backdoor failures are implanted during the training phase, including detection of poisoned samples and trigger mechanisms.
Our defense protocol uses continued training on verified, clean data to neutralize hidden triggers without requiring prior knowledge of backdoor mechanisms.
We identify mathematical scaling laws that predict cleanup difficulty as a function of model size, providing defenders with cost estimation frameworks.
The more poison training a model receives, the more cleanup is required. Larger models demonstrate exponentially greater resistance to remediation efforts, making advanced systems especially vulnerable to persistent backdoors.
The only comprehensive solution for detecting, analyzing, and removing adversarial backdoors from production AI systems.
Identify hidden triggers and poisoned training samples with 99.7% accuracy
Remove backdoors through verified continued training protocols
Predict cleanup costs and effort for models of any size
Real-time threat detection across your entire AI deployment
DoD-compliant documentation and audit trails
On-premise solutions for classified environments
Backdoor persistence is just one of many failure modes that today's alignment methods leave unresolved. Making AI truly dependable in defense contexts requires alignment research on underexplored approaches — work that the major AI labs are not pursuing.
Experience our detection engine analyzing a compromised model in real-time.
This demo analyzes a deliberately compromised model. Your production systems may already contain similar vulnerabilities.
Request a confidential threat assessment and briefing on our research. Available for DoD, Intelligence Community, and select defense contractors.