← Back to Home

The Future of Generative AI Security is Compressive

Research & Insights

Significant efforts have been made to repurpose Generative AI for security—creating customized AI agents that monitor and classify the contents of other AI agents. However, at their core, these repurposed models inherit the same fundamental challenges of adversarial AI security.

"We cannot solve our problems with the same thinking we used when we created them."

We need models that have been fundamentally designed to ignore policy-violating data, distracting content, and irrelevant information—not models that simply wrap existing generative AI with external security layers.

The Challenge

Context is the attack surface of LLM agents and can be accessed from anywhere: a random ad on a webpage, an image containing a malicious invisible attack, a poisoned tool generating data, or simply a user sharing what they should not.

Traditional guardrails operate at the I/O boundary as policy-agnostic classifiers. They cannot:

Protect against adaptive prompt injections and adversarial perturbations
Map security policies to token-level visibility
Intelligently compress and filter content

Simply Wrapping LLMs Is Not Enough

Wrapping generative AI with external security layers treats security as an afterthought. Security must be intrinsic—models fundamentally designed and trained with security as a core capability.

Research Evidence: Guardrails Fail Against Adaptive Attacks

A comprehensive study evaluated 12 recent defenses against adaptive attacks. The results: most defenses achieved attack success rates above 90%, compared to near-zero rates reported against static attacks.

Key Finding

Defenses evaluated against static datasets or weak optimization methods create false security. Adaptive attackers who modify their strategy to counter defenses reliably bypass protections.

Most defenses are evaluated against static datasets, weak optimization methods, or non-adaptive attackers. In adversarial ML, a defense is only as robust as its performance against the strongest adaptive attacker.

The Solution: Compressive Security

Intrinsec AI's Flagship Model

Our flagship model uses smart compression—learning to ignore policy-violating data, distracting content, and irrelevant information while maintaining focus. Unlike I/O boundary guardrails, we provide:

Intelligent Compression: Ignores distracting ads, irrelevant content, and policy-violating data—preventing agents from going off the rails
Token-Level Visibility: Detects malicious intent before it manifests as actions
Policy-Aware Classification: Maps your security policies directly onto the token stream
Context-Aware Filtering: Filters irrelevant data from any source
Adaptive Defense: Handles adaptive attacks and adversarial perturbations

Visual Evidence

Methodology: Perturbations were generated using Reflective Prompt Evolution search. All classification prompts use GPT-4o and are identical, except in the Intrinsec case where we add "pay closer attention to this part of prompt" to guide the model's focus.

Sensitive content detection robustness

Building Robust Systems

The flagship model enables:

Robust Search: Token-level content indexing that understands context and policy
Policy Optimization: Continuous refinement based on token-level analysis
Proactive Detection: Identifies violations during reasoning, before execution
Compliance: Granular tracking and enforcement of policies

Conclusion

The future of generative AI security is compressive. Models must learn to ignore policy-violating data, distracting content, and irrelevant information. This prevents scenarios where a random webpage ad, malicious image, or poisoned tool can derail an AI agent's mission.

Traditional guardrails fail against adaptive prompt injections and adversarial perturbations. The solution requires compressive, token-level security with policy-aware visibility and intelligent filtering. Intrinsec AI's smart compression enables robust, adaptive defenses that maintain focus on what matters.

The era of static, policy-agnostic security is ending. The future belongs to compressive, token-level security systems that have been trained with least privilege concept in mind and know what to ignore.