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Securing AI should not be a tight one-size-fits-all, since very business has a unique AI use case and business requirements. Your business needs should guide how AI is constrained within your environment, allowing you to utilize AI for increased productivity. Euler One's ability to constrain arbitrary concepts in models ensures this, helping you unlock your business potential without worries.As AI threats rise, authorities pass new regulatory policies unique to the industry domain. E.g., AI models deployed to assist health practitioners in medical organizations should not divulge PII, per HIPAA laws. Euler One helps to enforce this. We integrate policy templates for existing regulations unique to specific industries, helping you stay complaint with evolving security regulatory standards in this AI revolution.
Traditional defenses cannot secure modern AI applications: As LLMs becomes ubiquitous
Recent works show that modern Large Language Models (LLMs) are vulnerable to a variety of security exploitation techniques (see OWASP Top 10 for LLMs) such as prompt injection and supply chain vulnerabilities. Current solutions provide "work-around" mitigations (such as filtering prompts) rather than tackle the underlying hard problem. Still, advanced attacks such as LLM backdoors have no known defense as there is no way to detect them. LLM backdoors are hidden malicious capabilities in the model, which can be triggered in arbitrary ways by an attacker, enabling him to evade the model's trained guardrails and subvert its protective alignment. This poses serious safety and security consequences to the enterprise and the world. Unlike traditional security breaches, the risk of LLM compromise is exponentially greater because it gives asymmetrical capabilities to the attacker, who can perform a far more greater range of functionalities embedded in modern LLMs.
The hard challenge in protecting LLMs stems from the fact that AI models are mostly black boxes. Unlike traditional software programs, they do not contain code that can be analyzed to reveal their range of behaviors, but billion of mathematical objects called weight parameters, which are non-trivial to understand. Worse, AI responses and decision-making lack explain-nability -- an open research problem. Hence, since it is not tractable to analyze the learned capabilities of LLMs in order to identify and "patch" potentially malicious or safety-relevant behavior, this presents a formidable challenge to ensuring their security and safety.
As LLMs becomes ubiquitous, attacker have never seen a more attractive opportunity to implant hidden capabilities while been hidden. Without defenses, adversaries can influence AI decision making and perturb their responses at will, which can impact downstream applications an dependent processes. Following the initial awareness of LLM security risks, security providers rushed to provide ad-hoc fixes centered around monitoring and filtering LLM prompts and responses with firewalls. Unfortunately, these work-arounds do not fix the underlying problem and don't stand a chance against an intentional adversary.
Euler One tackles the underlying problem based on novel mathematical foundation-based understanding of LLM internals. Through this, Euler One can identify safety and security vulnerabilities in LLMs to enable targeted patching. By combining cross-cutting fundamentals in math and security, Euler One identifies where an LLM is prone to bypassable model alignment, weak and loosened guardrails, abuse-vulnerable concepts, and backdoor injection.
Euler One analyzes LLMs to identify concepts (e.g., PII, hate speech, criminal content, other compliance-mandated regulation per industry) whose safety and security guardrails trained into the model during model alignment and evaluation, can be circumvented by an attacker.
Euler One can tailors its security analysis of LLMs based on the customer's policy, security compliance need, and business environment. Through this, Euler takes a holistic business-aware approach to protect users, customers, and enterprises from LLM risks. Euler One provides tools for continuous security and actionable metrics throughout the lifecycle of a customers LLM usage, such as fine-tuning their LLM for additional business need, or adapting it for other business applications.