Guardrails for AI: Securing Language Models with Intelligent Agents

by

Author: Denis Avetisyan

As large language models become increasingly integrated into applications, a robust security framework is crucial, and this paper explores a novel approach using multi-agent systems to address common vulnerabilities.

This review details a framework leveraging Microsoft AutoGen and Retrieval Augmented Generation to mitigate the OWASP Top 10 security risks for Large Language Model applications.

While Large Language Models (LLMs) unlock unprecedented capabilities in natural language processing, their widespread adoption introduces significant security vulnerabilities. This paper, ‘Mitigating the OWASP Top 10 For Large Language Models Applications using Intelligent Agents’, addresses this challenge by presenting a novel framework leveraging LLM-enabled intelligent agents to proactively counter the threats outlined in the OWASP Top 10. Our approach utilizes multi-agent systems, specifically incorporating Microsoft AutoGen and Retrieval Augmented Generation, to identify and mitigate vulnerabilities in real-time. Could this framework establish a new paradigm for securing LLM applications and fostering trust in this rapidly evolving technology?

Unveiling the Data Shadows: LLMs and the Privacy Paradox

The proliferation of Large Language Models (LLMs) introduces unprecedented challenges to data privacy, stemming from their inherent need for vast datasets during both training and operation. These models, capable of processing and generating human-like text, often ingest sensitive information – from personal identifiers and financial records to confidential communications – increasing the risk of data breaches and misuse. Unlike traditional data storage, LLMs don’t simply store data; they learn from it, potentially memorizing and reproducing sensitive details in their outputs. This poses a particular difficulty because identifying and removing such memorized information is a complex technical hurdle, and standard data anonymization techniques may prove ineffective against the sophisticated pattern recognition capabilities of these models. Consequently, organizations adopting LLMs must navigate a delicate balance between leveraging their power and safeguarding the privacy of individuals whose data fuels their functionality.

The integration of Large Language Models into educational settings is complicated by long-standing regulations designed to safeguard student privacy. Laws such as the Family Educational Rights and Privacy Act (FERPA) establish stringent requirements regarding the handling of personally identifiable information, demanding explicit consent for data sharing and limiting access to student records. This presents a substantial challenge for LLMs, which often require large datasets for training and operation; simply feeding student data into these models risks violating FERPA’s provisions. Institutions seeking to leverage the benefits of LLMs must therefore navigate a complex landscape of compliance, implementing robust de-identification techniques, secure data storage solutions, and carefully crafted data usage agreements to ensure student data remains protected while still enabling effective AI-powered learning experiences.

The consequences of insufficient data protection extend far beyond simple compliance failures. Organizations that experience data breaches or demonstrate lax security protocols face substantial legal repercussions, including hefty fines and costly litigation. Beyond the financial burden, such incidents invariably inflict significant reputational damage, as consumers and partners lose faith in an entity’s ability to safeguard personal information. This erosion of trust can lead to customer attrition, diminished brand value, and long-term difficulties in attracting new business. Ultimately, a compromised approach to data privacy doesn’t just risk legal and financial penalties; it fundamentally undermines the relationship between an organization and those it serves, fostering a climate of skepticism and distrust that is exceedingly difficult to overcome.

Fortifying the Walls: Security Policies and Input Controls

A comprehensive Security Policy for Large Language Models (LLMs) establishes a foundational framework for data handling, outlining acceptable use, access controls, and security standards. This policy must detail procedures for data classification, storage, and transmission, encompassing both input and output data streams. It should specify authorized user roles and associated permissions, as well as protocols for responding to security incidents. Furthermore, the policy requires regular review and updates to address evolving threats and vulnerabilities, and must clearly define compliance requirements and consequences for violations. A well-defined policy ensures consistent application of security measures across the entire LLM lifecycle, mitigating risks associated with data breaches, unauthorized access, and malicious exploitation.

Input validation is a critical security measure implemented to examine all data submitted to a system against a predefined set of rules and constraints. This process confirms data type, length, format, and range, rejecting any input that does not conform to these specifications. By strictly enforcing these rules, input validation effectively prevents several attack vectors, including SQL injection, cross-site scripting (XSS), and buffer overflows. Validating input at the point of entry minimizes the risk of malicious data reaching backend systems or being processed by the Large Language Model (LLM), thereby safeguarding data integrity and system security. Furthermore, proper input validation reduces the potential for denial-of-service attacks stemming from malformed or excessively large inputs.

Policy-driven input control for Large Language Models (LLMs) operates by establishing a set of pre-defined rules and criteria that all incoming data must satisfy before processing. This involves verifying data type, length, format, and allowable character sets against the stipulations outlined in the security policy. Data failing these validations is either sanitized, rejected, or flagged for review, preventing potentially malicious or improperly formatted inputs from reaching the LLM. By strictly enforcing these rules, the system limits the LLM’s exposure to unexpected or harmful data, thereby reducing the risk of prompt injection attacks, data exfiltration, and other security vulnerabilities, while also maintaining data integrity and operational stability.

Examining the Output: Validation and Access Control as Guardrails

Output validation is a crucial security measure for Large Language Models (LLMs) because these models can inadvertently generate responses containing sensitive data present in their training datasets or unintentionally reveal information violating pre-defined security protocols. This validation process involves analyzing the LLM’s output for the presence of personally identifiable information (PII), confidential business data, or responses that could facilitate malicious activity. Techniques employed include regular expression matching, keyword filtering, and the use of dedicated data loss prevention (DLP) systems. Thorough validation minimizes the risk of data breaches, ensures compliance with privacy regulations, and maintains the integrity of the LLM application by preventing the dissemination of harmful or unauthorized content.

Access control mechanisms for Large Language Models (LLMs) function by implementing authentication and authorization protocols to restrict data access. Authentication verifies the identity of a user or application requesting data, while authorization determines the specific data and operations that authenticated entity is permitted to access. These mechanisms commonly utilize Role-Based Access Control (RBAC), assigning permissions based on predefined roles, or Attribute-Based Access Control (ABAC), granting access based on user attributes, data characteristics, and environmental conditions. Implementation includes API keys, OAuth 2.0, and fine-grained permission settings within the LLM’s deployment environment, ensuring that only authorized personnel can query or modify sensitive data and that data exposure is minimized by limiting the scope of access granted to each user or application.

A robust security posture for Large Language Models (LLMs) necessitates a layered defense strategy addressing both data ingress and egress. Input validation, focusing on sanitizing prompts and restricting access to sensitive data sources, constitutes the initial defensive layer. Complementing this, output validation and access control mechanisms form the exit-point defense, scrutinizing generated responses for confidential information and ensuring that only authorized users receive specific outputs. This dual approach minimizes the risk of both data breaches stemming from malicious inputs and unintended data leakage through LLM-generated text, creating a more resilient system against a variety of threat vectors.

Orchestrating Vigilance: A Multi-Agent System for Proactive Security

A novel Multi-Agent System architecture is proposed to address the escalating security challenges within Large Language Model (LLM) workflows. This system moves beyond traditional, monolithic security measures by distributing responsibility among a network of specialized conversational agents. Each agent is designed to enforce specific security policies at various stages of the LLM process – from initial prompt analysis and data validation to output sanitization and threat detection. This collaborative approach allows for continuous monitoring and proactive mitigation of vulnerabilities, creating a dynamic security layer that adapts to evolving threats. By leveraging the strengths of multiple agents working in concert, the system aims to significantly enhance the robustness and resilience of LLM applications against a wide range of security risks. We are not simply building walls; we are cultivating a vigilant ecosystem.

The system’s architecture is built upon the AutoGen framework, a powerful tool that facilitates the creation of multi-agent conversations and workflows. This implementation allows for the distribution of security tasks among specialized agents, each designed to address specific vulnerabilities or policy requirements. Through collaborative problem-solving, these agents can autonomously analyze LLM inputs and outputs, identify potential threats, and implement appropriate safeguards. Automated security checks, orchestrated by the framework, ensure consistent and reliable enforcement of security policies throughout the entire LLM workflow, reducing the need for manual intervention and bolstering the overall security posture of the application. This approach moves beyond reactive threat detection to a proactive system capable of anticipating and mitigating risks before they manifest.

A key innovation lies in the system’s distributed security architecture, where specialized agents collaboratively address vulnerabilities rather than relying on a single point of defense. This approach significantly enhances robustness and adaptability, allowing the system to respond effectively to evolving threats within Large Language Model (LLM) applications. Research demonstrates a proactive mitigation of risks detailed in the OWASP Top 10, including injection attacks, broken authentication, and sensitive data exposure. By assigning distinct security responsibilities – such as input validation, output sanitization, and threat detection – to individual agents, the system achieves a layered defense that is more resilient to sophisticated attacks and capable of maintaining a secure LLM workflow.

The pursuit of security, as demonstrated by this framework mitigating the OWASP Top 10 for Large Language Models, echoes a fundamental principle: understanding through dissection. The paper doesn’t merely apply security measures; it actively probes for weaknesses, employing intelligent agents to reverse-engineer potential vulnerabilities within LLM applications. This mirrors a hacker’s mindset – a controlled demolition to reveal structural flaws. Blaise Pascal observed, “The eloquence of angels is a silence.” Similarly, true security isn’t about loud defenses, but a quiet, comprehensive understanding of attack surfaces – a silent anticipation of failure modes uncovered through rigorous, agent-driven testing, particularly through techniques like Retrieval Augmented Generation, to proactively address those vulnerabilities.

Beyond the Checklist

The pursuit of security, particularly when applied to the emergent chaos of large language models, often resembles a frantic attempt to bolt locks onto a sandcastle. This work, by framing vulnerability mitigation as an exercise in multi-agent negotiation and retrieval augmentation, acknowledges a crucial truth: static defenses are, by their nature, overtaken. The OWASP Top 10, while a useful catalog of predictable failures, represents only the known weaknesses. The real challenge lies in anticipating the novel attack vectors that will inevitably arise from the unpredictable interplay between these models and adversarial actors.

Future investigation should move beyond simply addressing listed vulnerabilities and instead focus on building systems capable of detecting anomalous behavior – systems that treat every interaction as a potential probe. This necessitates a shift from prescriptive security rules to adaptive, learning-based defenses. The agentic framework presented here offers a promising avenue, but the true test will be its resilience against attacks specifically designed to exploit the agents themselves – turning the tools of defense into avenues of compromise.

Ultimately, the most valuable outcome of this line of inquiry may not be a perfect security solution, but a deeper understanding of the inherent limitations of control when dealing with complex, evolving systems. It is in acknowledging what cannot be secured that one begins to truly understand the landscape of risk.

Original article: https://arxiv.org/pdf/2601.18105.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

See also:

2026-01-27 19:28