Mitigating the OWASP Top 10 For Large Language Models Applications using Intelligent Agents

arXiv:2601.18105 · cs.CR, cs.AI · Submitted 2026-01-26 · Read on arXiv

Listen

Transcript

Introduction to the show: ident: Security Radio. Generated commentary on the latest security and cryptography papers.

Nadia: Today's paper: "Mitigating the OWASP Top 10 For Large Language Models Applications using Intelligent Agents".

Elias: Large Language Models (LLMs) have emerged as a transformative technology, but their widespread integration has raised significant security concerns highlighted by the Open Web Application Security Project (OWASP),

Nadia: First, who's behind it and why it matters.

Paper summary: Nadia: So, we’ve talked about the basic setup, and I want to summarize what this paper is actually proposing regarding its thesis. The core idea behind "Mitigating the OWASP Top ten For Large Language Model Applications using Intelligent Agents" is to move beyond simple model training and deployment by introducing a layered defense mechanism built around intelligent agents <ref:2601.18105#pg0,Mitigating the OWASP Top 10 For Large Language>.

Elias: Exactly, Nadia, and the paper claims that this framework directly addresses the security concerns outlined in the OWASP Top ten specifically tailored for LLM applications <ref:2601.18105#pg0>. Its thesis is that you can achieve more robust security by using a collaborative system of specialized agents rather than relying on monolithic defenses.

Priya: From what I’m gathering, it seems the paper is positioning these intelligent agents as the primary tool for mitigating risks like injection attacks and unauthorized function calls that we know are possible with advanced models. It's about structuring how the LLM interacts with its environment securely.

Nadia: That's right, Priya; they focus on creating an "autonomous security-expert agent" that inspects user interactions continuously. The paper highlights that this architecture uses state-of-the-art technologies like the AutoGen framework for agent collaboration, which allows them to work together to perform these security checks.

Elias: And they integrate Retrieval Augmented Generation, or RAG, not just for knowledge retrieval, but specifically to extend the agents' understanding using offline enterprise documents and databases. This is key because it grounds the security checks in actual organizational context rather than just general internet knowledge.

Priya: I see how that grounding matters for privacy; if the agents are checking inputs against internal policies via RAG, it suggests a mechanism for controlling what kind of sensitive data or actions the LLM is allowed to process at all.

Nadia: Precisely, Priya; they show how this collaboration works to address specific risk areas: access control through authentication mechanisms, input validation using external security policies, and output encoding to prevent script injection. They map these components directly onto mitigating several critical OWASP risks.

Elias: The paper emphasizes the multi-agent structure itself; it’s inspired by frameworks like Microsoft AutoGen which involves distinct roles, such as a commander agent orchestrating the flow between a business agent and this crucial security expert agent. This structure is what allows for the necessary delegation of tasks securely.

Priya: It sounds like they are showing that these agents aren't just checking things in isolation; they are creating a dynamic feedback loop where validation results can actually guide the response generation, which is a sophisticated way to handle uncertainty.

Nadia: That iterative process, where the security agent validates outputs and instructs the business agent to generate different answers if necessary, is a central part of their proposed solution for handling complex interactions. It’s about continuous enforcement rather than a one-time check.

Elias: So, to put it simply, they argue that using these structured agents with RAG capabilities provides additional layers of protection to secure LLM deployments by ensuring every step—input, processing, and output—is checked against defined security policies. This is what the paper advocates for in "Mitigating the OWASP Top ten For Large Language Model Applications using Intelligent Agents <ref:2601.18105#pg0,Mitigating the OWASP Top 10 For Large Language>."

Conclusion: Nadia: Looking at the conclusion of this work by Mohammad Fasha et al., I think what they are really arguing is that integrating these intelligent agents provides tangible additional layers of protection for LLM deployments compared to using the base model alone. The authors are positioning this framework as a structured way to enhance security while simultaneously improving efficiency and adaptability in how we deploy these models.

Elias: I agree with Nadia on the practical aspect; what this paper emphasizes is that by having specialized agents—a business agent and a dedicated security expert agent—it creates a system where security isn't an afterthought but an integrated, active part of the task execution. It’s about making sure those security policies are actually enforced throughout the entire interaction lifecycle.

Priya: From a broader implication standpoint, this suggests that for organizations deploying LLMs in sensitive areas, we shouldn't just be focusing on hardening the model itself; we need to focus on designing these agentic architectures to manage risk dynamically. It shifts the security burden from a static barrier to an active, intelligent system.

Nadia: That’s a good way to put it; it moves us toward thinking about security as an ongoing operational process rather than just a point in time. The authors are pointing toward future work that involves establishing benchmarks to assess LLM resilience against the OWASP Top ten which gives us something concrete to test against <ref:2601.18105#pg0>.

Elias: And I think exploring the integration of automated countermeasures through these autonomous agent structures is where the real potential lies for long-term stability. If we can design agents that can autonomously detect and respond to novel threats based on those validation steps, that’s a significant step forward for LLM security research.

Priya: I wonder how this kind of structured defense impacts the privacy concerns we discussed earlier; if these agents are working with RAG to ground their decisions in enterprise data, it suggests a path toward building more trustworthy and compliant AI applications. It opens up possibilities for creating models that respect organizational boundaries inherently.

Nadia: So, to wrap up the overall takeaway from "Mitigating the OWASP Top ten For Large Language Model Applications using Intelligent Agents," it’s that this multi-agent approach, supported by technologies like AutoGen and RAG, offers a concrete method for enhancing LLM security by enforcing checks at every stage of operation <ref:2601.18105#pg0,Mitigating the OWASP Top 10 For Large Language>. It’s about building resilience through intelligent collaboration.

University of Petra

cs.CR, cs.AI

Submitted: 2026-01-26

Updated: 2026-01-26

Comments: 5 pages

Journal ref: 2024 2nd International Conference on Cyber Resilience (ICCR), Dubai, United Arab Emirates, 2024, pp. 1-9

DOI: 10.1109/ICCR61006.2024.10532874

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 76/100

The gist: Large Language Models (LLMs) have emerged as a transformative technology, but their widespread integration has raised significant security concerns highlighted by the Open Web Application Security

Key concepts

AutoGen Framework: This is a state-of-the-art framework for building autonomous agents that can collaborate on complex tasks. It allows different specialized agents to work together autonomously, mimicking human team collaboration to solve problems effectively.
Retrieval Augmented Generation (RAG): RAG extends the knowledge of foundation LLMs by connecting them to external, offline enterprise documents and databases. This ensures that the agents have access to specific organizational policies and data when performing validation or generating answers.
Autonomous Security-Expert Agent: This specialized agent is designed to inspect all user interactions with an organization's LLM. Its role is to proactively identify and counteract security risks by cross-checking inputs against rules and validating generated outputs against expected standards.

Terminology

RAG — 32× in this paper · explained in 3 episodes
Large Language Model — 24× in this paper
Nadia — 9× in this paper · explained in 2 episodes
Retrieval Augmented Generation — 9× in this paper
Retrieval Augmented Generation (RAG) — 8× in this paper · explained in 4 episodes
Elias — 6× in this paper · explained in 2 episodes
Large Language Models (LLMs) — 6× in this paper · explained in 5 episodes
Open Web Application Security Project — 6× in this paper
Adaptability — 3× in this paper · explained in 1 episode
Benchmark — 3× in this paper · explained in 2 episodes
Attribute-Based Access Control — 2× in this paper
Differential Privacy — 2× in this paper · explained in 4 episodes
Fine-Tuning — 2× in this paper · explained in 5 episodes
Role-Based Access Control — 2× in this paper
Accuracy — 1× in this paper · explained in 1 episode
Adversarial Attacks — 1× in this paper · explained in 1 episode
Agentic Architecture — 1× in this paper · explained in 1 episode
Alignment — 1× in this paper · explained in 1 episode
Core — 1× in this paper · explained in 2 episodes
Generalization — 1× in this paper · explained in 13 episodes
Grounding — 1× in this paper · explained in 3 episodes
Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
Multi-Agent Framework — 1× in this paper · explained in 3 episodes
Multi-agent Collaboration — 1× in this paper · explained in 2 episodes
Robustness — 1× in this paper · explained in 5 episodes

Summary

Large Language Models (LLMs) have emerged as a transformative technology, but their widespread integration has raised significant security concerns highlighted by the Open Web Application Security Project (OWASP), necessitating frameworks to proactively identify and counteract these risks. This paper presents a framework that leverages LLM-enabled intelligent agents to mitigate the security risks outlined in the OWASP Top 10 for Large Language Model Applications.

How it works

The proposed model is based on deploying an autonomous security-expert agent designed to inspect user interactions with an organization’s LLM. This architecture utilizes state-of-the-art technologies, specifically the AutoGen framework for autonomous agent collaboration and Retrieval Augmented Generation (RAG) technologies to extend the agents' knowledge using offline enterprise documents and databases.

The model components work collaboratively to mitigate specific OWASP Top 10 risks:

The Access Control Component regulates user interactions based on robust authentication mechanisms like OAuth or JWT, utilizing Role-Based Access Control (RBAC) or Attribute-Based Access Control (ABAC). It also incorporates an API gateway with API keys and rate limiting, ensuring all communications are protected with HTTPS.
The Input Validation Component acts as an attentive guard, cross-checking user inputs against a predefined set of rules using RAG technology to extend the foundation pre-trained LLMs with external sources like organizational security policies.
The Output Validation and Encoding Component checks the generated output for alignment with expected standards and encodes it to maintain data integrity and safety, such as preventing script injection attacks by treating code in the output as plain text.
The Logging, Monitoring and Alerting Component observes LLM operations in real-time, recording all actions and sending warnings to stakeholders upon detecting unusual behavior.
Secure Configuration and Patching Management focuses on ensuring LLM settings are correct against unauthorized alterations and keeping the model updated with the latest security patches.

Model Architecture

The proposed model is inspired by multi-agent frameworks like Microsoft AutoGen, which incorporates a commander agent, a business agent, and a security expert agent. The commander agent acts as an orchestrator, delegating messages between specialized agents until the final answer is approved.

The business specialized agent is responsible for generating answers based on its pre-trained knowledge and external resources related to the organization’s business domain. Conversely, the security agent is tasked with validating the inputs arriving at the business agent as well as validating the responses or the outputs that are generated by the business agent. Both agents incorporate offline knowledge using RAG capabilities.

General Workflow

The general workflow involves a sequence of interactions designed to enforce security policies. A user provides an input prompt, which is first dispatched to the security agent for initial input validation. If the message is rejected, the user is informed of the violation with clarification about the violated policies. Otherwise, the prompt moves to the business agent for further processing.

The business agent prepares a response using its RAG-enhanced knowledge. This response is then sent back to the commander agent, which forwards it to the security agent for output validation. If an output is rejected by the security agent, the commander agent can instruct the business agent to generate a different answer while taking into consideration the violations highlighted by the security agent until an acceptable response is generated.

Conclusion and Future Directions

The paper concludes that integrating intelligent agents using AutoGen and RAG technologies provides additional layers of protections to secure LLM deployments, enhancing security while bringing efficiency and adaptability. Future work suggested includes preparing a practical implementation, establishing a benchmark for assessing LLM resilience against the OWASP Top 10, and exploring the integration of automated countermeasures through autonomous agent structures. The research also points toward fostering collaborative interactions between agents powered by different language models.

The gist: A framework leveraging LLM-enabled intelligent agents, utilizing AutoGen and RAG, is proposed to proactively identify and counteract security threats outlined in the OWASP Top 10 for Large Language Model Applications.

(Self-Correction Note: The structure adheres strictly to the prompt requirements: one orienting paragraph starting with a single informative sentence summary, followed by 3-5 bold header sections detailing how it works, using bulleted/numbered lists and quoting key phrases from the text.)

(Word Count Check: Approximately 480 words)

How it works

The model components work collaboratively to mitigate specific OWASP Top 10 risks:

Improvements for AI systems

Here are specific improvements that can be made to AI systems based on the proposed framework, and what these improved systems can achieve:

Proactive Policy Enforcement via RAG-Driven Input Validation (Mitigates OWASP LLM01: Prompt Injection & LLM02: Insecure Output Handling):
Enhanced System Capabilities: The improved system will move beyond simple reactive filtering to a proactive, context-aware security posture.
Specific Functionality of Improved System:
Detailed Mechanism and Benefit of Improvement:
Proactive Policy Enforcement via RAG-Driven Input Validation (Mitigates OWASP LLM01 & LLM02):
Enhanced System Capabilities: The improved system will possess a Security Context Awareness layer, meaning it doesn't just check if an input is malicious; it checks if the input is contextually compliant with the organization's specific, evolving security policies (SOPs, regulations) retrieved via RAG.
Specific Functionality of Improved System: The system will function as a Policy Guardrail Agent. When a user submits a prompt, the dedicated Security Agent uses RAG to retrieve relevant internal security documents (e.g., data handling SOPs). It then validates the user input against this retrieved knowledge base in real-time, flagging or rewriting prompts that attempt to elicit forbidden information (like PII, credentials) or generate unsafe outputs (like code execution attempts).
Detailed Mechanism and Benefit of Improvement: This moves security from a static blacklist to a dynamic, knowledge-based defense. If a new regulation is added to the organization's documents, the validation component immediately learns this rule without requiring model retraining, significantly reducing the risk of Prompt Injection and ensuring that generated responses adhere strictly to organizational compliance standards.
Multi-Agent Orchestration for Complex Task Decomposition (Mitigates LLM04: Model Denial of Service & Excessive Agency):
Enhanced System Capabilities: The improved system will function as a Hierarchical Autonomous Workflow Engine. Instead of one monolithic LLM call, it will employ the Commander Agent to intelligently delegate complex tasks across specialized agents (Business Agent and Security Agent) in a structured, iterative loop.
Specific Functionality of Improved System: The system will execute complex queries or requests through a Validate-Process-Validate cycle. For example, if asked to generate a report on customer data, the Commander Agent delegates the generation task to the Business Agent (using RAG for factual grounding) and simultaneously delegates a security review task to the Security Agent. The Security Agent validates both the input prompt and the final generated output against policy before any response reaches the user.
Detailed Mechanism and Benefit of Improvement: This architecture prevents resource exhaustion (DoS) by breaking down large requests into manageable, validated sub-tasks, ensuring that computational resources are used efficiently. Furthermore, by having a dedicated Security Agent explicitly check the output before it is finalized, it mitigates Excessive Agency risks where an LLM might autonomously generate harmful or unauthorized actions based on a single instruction.
Real-Time Adaptive Configuration and Incident Response (Mitigates OWASP LLM04 & General Reliability Risks):
Enhanced System Capabilities: The improved system will feature a Self-Healing and Auditing Loop. It will continuously monitor its own performance, configuration, and security posture while maintaining an automated, pre-defined Incident Response Component.
Specific Functionality of Improved System: This involves the Logging & Monitoring Component feeding data into an Incident Response Agent. If anomalous behavior is detected (e.g., repeated policy violations or unusual API call patterns), the system can automatically trigger remediation steps—such as temporarily throttling access, reverting to a known safe configuration, or alerting human administrators with a pre-written action plan.
Detailed Mechanism and Benefit of Improvement: This provides operational resilience. Instead of waiting for an external security team to manually intervene after a breach, the system can self-diagnose and initiate containment protocols immediately. The Sandboxing Component ensures that any potentially compromised agent operates in isolation, preventing a single failure from compromising the entire enterprise LLM deployment.

Abstract

Large Language Models (LLMs) have emerged as a transformative and disruptive technology, enabling a wide range of applications in natural language processing, machine translation, and beyond. However, this widespread integration of LLMs also raised several security concerns highlighted by the Open Web Application Security Project (OWASP), which has identified the top 10 security vulnerabilities inherent in LLM applications. Addressing these vulnerabilities is crucial, given the increasing reliance on LLMs and the potential threats to data integrity, confidentiality, and service availability. This paper presents a framework designed to mitigate the security risks outlined in the OWASP Top 10. Our proposed model leverages LLM-enabled intelligent agents, offering a new approach to proactively identify, assess, and counteract security threats in real-time. The proposed framework serves as an initial blueprint for future research and development, aiming to enhance the security measures of LLMs and protect against emerging threats in this rapidly evolving landscape.