As GenAI systems continue to be rapidly adopted across industries, organizations are embedding LLMs into production-facing applications at unprecedented rates. These models serve as intelligent interfaces for internal tools, customer support platforms, recommendation engines, and data analytics. Yet, like early third-party software integration, LLM application security faces evolving threats requiring dedicated protection.
Just as security embraces different mitigation layers like SCA for third-party risks and runtime protections such as EDR and ASLR for active threats, Gen AI security now demands specialized safeguards. Model-built guardrails are necessary but insufficient against evolving adversarial prompt techniques and manipulation strategies.
Consequently, dedicated AI runtime security tools tailored to LLMs are essential. Real-time monitoring and adaptive defense mechanisms ensure secure LLM integration as both models and threats continue evolving.
LLMs – A New Attack Surface
Security vulnerabilities remain intrinsic to software development, continuously surfacing as technology evolves. From early flaws like Shellshock in Bash and Heartbleed in OpenSSL to thousands of new vulnerabilities identified annually, these incidents underscore the necessity for robust security measures and continuous vigilance. As LLMs become integral to application functionality, new vulnerability classes emerge. Techniques like crescendo, GCG, and refusal suppression exemplify the evolving threat landscape. These attacks manipulate model outputs, exfiltrate data, or bypass safety mechanisms, causing data leakage, financial loss, and reputational harm.
The Real-World Impacts of LLM Vulnerabilities
Business Logic Exploitation:
A Chevrolet dealership’s AI chatbot was manipulated into agreeing to sell a $76,000 Chevy Tahoe for $1, By crafting prompts it bypassed safeguards, leading to unauthorized pricing agreements and forcing dealership shutdown.
Sensitive Data Leakage
Samsung employees used ChatGPT for tasks, inadvertently inputting sensitive information including proprietary source code and internal meeting notes. This data was stored on external servers, exposing confidential company information.
Operational Resource Abuse
Attackers exploit LLMs to perform unauthorized tasks by crafting prompts that generate extensive, resource-intensive outputs continuously, effectively repurposing models for crypto mining or similar unauthorized objectives.
Unauthorized Access and Attack Vectors
Improperly secured LLMs can reveal sensitive internal system information through crafted prompts disclosing backend architectures, API structures, or system configurations. LLMs can serve as conduits for further attacks, generating malicious inputs like SQL injection payloads executed by connected services.
Where Existing Solutions Fall Short
Built-in Model Guardrails
Built-in guardrails from model creators like OpenAI or Anthropic update with each training iteration, addressing the previous cycles threats. This iterative process means defenses inherently lag behind rapidly evolving attack vectors. Organizations relying solely on built-in safeguards face significant protection delays against new threats.
Cloud Provider Guardrails
Cloud provider LLM application security solutions face significant limitations. Although updated faster than model creators, response time remains insufficient against emerging threats. Cloud providers lack granular control and customization for organizational needs, prioritize DevOps workflows over security professionals, and tie organizations to specific ecosystems, limiting uniform security strategies across multiple environments.
Customer-Built Solutions
Customer-built solutions offer high customization but raise critical questions about team bandwidth and expertise for rapidly evolving Gen AI security threats. Maintaining effective defenses demands constant vigilance, deep expertise, and ongoing adaptation. Dedicated experts who analyze and defend against thousands of real-world attacks enable internal teams to focus on core product development while ensuring comprehensive AI runtime security.
Securing LLMs with AIDR: Real-Time Protection for Existing And Evolving Threats
Here enters Noma’s AIDR (AI Detection and Response). As an integral part of the most comprehensive AI security platform offered by Noma, our solution is designed specifically to address the security needs of LLM-based applications and provides a multi-layered defense strategy.
Detection And Anonymization Of Sensitive Data – Our AIDR solution is capable of identifying sensitive data being leaked to your LLM or end user, and can effectively respond to or mask it in a customizable manner.
Topic Guardrails – Dedicated detectors can make sure that LLM conversations are being kept within the boundaries of what your product is meant to provide, and will prevent any misuse, malicious, or off-topic conversation attempts.
Prompt Defender – Proactively intercepts jailbreaks, prompt-injection, and other manipulation attempts, shielding both the system prompt and the model itself.
Low Latency – Noma AIDR operates with minimal impact on performance, ensuring real-time protection without compromising user experience.
Rapid Vulnerability Adaptation – Our system swiftly adjusts to newly discovered vulnerabilities, ensuring timely updates to counter emerging threats.
Specialized Expertise – By leveraging our specialized knowledge in LLM security, your team can focus on core operations while we handle the complexities of threat detection and response.
Customization – Noma AIDR offers tailored security measures to fit your specific application needs, ensuring optimal protection aligned with your operational requirements.
Comprehensive Protection – Our solution encompasses prompt injection prevention, sensitive data leakage safeguards, and robust safety guardrails to address a wide range of security concerns.
Model-Agnostic Guardrails – Noma AIDR provides consistent security across various LLM platforms, ensuring reliable protection regardless of the underlying model.
Private Data Retention – We provide support for deployment within your organization’s infrastructure, ensuring that sensitive data stays entirely within your controlled environment.
Conclusion
Protecting GenAI applications demands more than incremental add-ons to yesterday’s security stack. LLMs introduce a constantly shifting attack surface, and the gaps in model-built guardrails, cloud-provider tooling, and ad-hoc in-house fixes leave organizations exposed. Noma AIDR closes that gap with purpose-built, real-time defenses that evolve as fast as the threats themselves, all while keeping performance tight and your data under your control. By off-loading LLM security to specialists, you free your teams to focus on what they do best—delivering innovative, AI-powered products with confidence that every prompt, response, and integration is protected today and ready for whatever comes tomorrow.
If you want to learn more about how Noma can help you on your AI security journey, please contact us.
