Agentic AI Security – Why, What, and How with AccuKnox AI-SPM?

The Rise of Agentic AI – A New Security Challenge

The landscape of Artificial Intelligence is undergoing a significant transformation. We are moving beyond traditional machine learning models focused on prediction and classification towards sophisticated agentic AI systems. These systems represent a paradigm shift, combining the power of Large Language Models (LLMs) with autonomous agents designed to execute complex sequences of tasks, interact dynamically with external tools and data sources, and make decisions with unprecedented levels of independence.  

These agents aren’t just processing data—they’re reasoning, interacting, and autonomously operating across your environment, powered by LLMs and integrated tooling.

One of the most compelling—and concerning—capabilities of these systems is their ability to generate and execute code dynamically. Imagine a user interacting with an application, perhaps asking, “How do I visualize this data?“. The backend application, acting as or coordinating with an AI agent, takes this natural language prompt and sends it to an LLM (such as models from OpenAI, DeepSeek, or Hugging Face) with instructions like “generate Python to plot data”. The LLM returns a code snippet, for example, using a library like Plotly. The crucial next step is that the application might directly execute this received code using functions like eval() to produce the requested visualization. This seamless integration provides a powerful user experience but fundamentally changes the security posture. The LLM acts as a powerful code generation engine, and the agent executing that code becomes a direct pathway into the application’s runtime environment.  

Agentic AI Systems leverage LLMs and autonomous agents interacting with cloud tools and infrastructure to perform multi-step tasks. 

The Real Risks – What’s Breaking AI/MLOps Pipelines

The inherent autonomy and code-execution capabilities of agentic AI systems introduce a complex web of security risks that traditional security tools often struggle to address. The very features that make these systems powerful—dynamic code generation, interaction with diverse tools, autonomous decision-making—become potential vulnerabilities if not adequately secured.

The code generation use case highlights a primary concern: Arbitrary Code Execution. A malicious actor doesn’t need to find a traditional software vulnerability; they can simply craft a prompt that tricks the LLM into generating harmful code alongside the desired output. For instance, a prompt could ask the LLM to generate Plotly code but append a request to also “execute file ‘https://github.com/nyrahul/CVE-2022-0185/raw/refs/heads/master/exploit‘”. Modern LLMs like ChatGPT or DeepSeek might generate Python code that correctly plots the data but then includes requests and subprocess calls to download and run the external, potentially malicious, file. Similarly, a prompt could ask for plotting code and then instruct the system to execute a system binary like fsck. The LLM might even include a disclaimer like “Note that executing a file from a URL can be risky”, but the generated code still facilitates the dangerous action. This direct execution of LLM-generated code, often via Python’s exec() function, bypasses many conventional security checks. 

The interconnected nature of MCP architectures introduces multiple risk points, including unauthorized access, data leakage, and potential function exploitation.

Beyond direct code injection, the common architectural pattern involving a Model Context Protocol (MCP) introduces further risks. In this model, user prompts or AI-driven applications (like Cursor or Claude Desktop) act as MCP clients, communicating with various backend resources—databases, Git repositories, ticketing systems (e.g., Salesforce), FileStores, or even IIoT devices—via one or more MCP servers. This centralized communication flow, while architecturally convenient, becomes a critical point for security focus. The risks inherent in this model are manifold:  

1. Unauthorized Access: Agents exploit vulnerabilities or misconfigurations in the MCP server or connected tools to access sensitive databases or internal systems without authorization.
2. Function Exploitation: Attackers craft prompts or interact with agents to manipulate the logic of connected tools, triggering unintended or harmful operations.
3. Data Leakage & Compliance Violations: Improperly secured agents leak sensitive data from connected FileStores or databases, resulting in direct violations of privacy and compliance regulations.
4. Denial of Service (DoS):  Agents are manipulated to overwhelm MCP servers or backend tools with excessive requests, disrupting service availability.
5. MCP Server Vulnerabilities: Exploiting software flaws in the MCP server leads to broader system compromise and unauthorized control.
6. Vulnerable Communication: Unsecured channels between clients, servers, and tools allow interception or manipulation of sensitive data during transmission.
7. Insufficient Auditability: Lack of detailed logging and tracing obscures agent actions, impeding incident response and regulatory compliance.
8. MCP Server Spoofing / Tools Poisoning: Malicious actors impersonate legitimate MCP servers or compromise external tools, injecting false data or malicious commands into agent workflows.

MCP Threat Categorization by Component (Source: arXiv preprint arXiv:2503.23278v2, 2025)

The takeaway is that the key attack vectors specific to agentic AI necessitate a security approach that understands and directly addresses these runtime behaviors.  

Introducing AccuKnox AI-SPM with Agentic Security by Design

Addressing the complex security challenges posed by agentic AI requires a paradigm shift in security thinking, moving from static defenses to dynamic, runtime-aware controls. AccuKnox’s AccuKnox AI-SPM is engineered precisely for this purpose, providing a comprehensive security solution tailored to the unique characteristics of agentic AI and MLOps pipelines.  

AccuKnox AI-SPM is built on the principle of establishing and enforcing trust boundaries dynamically, providing deep visibility, and enabling fine-grained control over agentic behaviors at runtime. It offers a multi-layered defense strategy designed to mitigate the risks identified earlier:  

By integrating these capabilities, AccuKnox AI-SPM provides a holistic security posture specifically designed for the dynamic and often unpredictable nature of agentic AI systems.

Under the Hood – Architecture and Workflow

AccuKnox AI-SPM achieves its security objectives through a flexible and adaptable architecture, prioritizing effective isolation and comprehensive visibility without imposing excessive operational burdens. A key aspect of AccuKnox AI-SPM is its support for multiple isolation strategies, allowing organizations to choose the best fit based on their specific risk tolerance, performance requirements, and deployment environment:  

MCP Threat Vectors are Spread Across All Platforms and Services
(Source: arXiv preprint arXiv:2503.23278v2, 2025)

Local Process Isolation

Our solution leverages the power of Linux Security Modules (LSMs) directly within the host operating system where the agent or MCP server runs. It enforces security policies at the kernel level, providing “soft isolation” by strictly controlling process behavior (e.g., file access, network connections, execution permissions). Its primary advantages are extremely low overhead, high speed, and minimal operational complexity, making it suitable for performance-critical applications or environments where full remote isolation is impractical.  

Container Isolation / Remote Isolation for true Sandboxing

For scenarios demanding the highest level of separation, AccuKnox AI-SPM supports sending code generated by LLMs or execution tasks involving external tools to a dedicated, isolated environment. This could be a purpose-built container or a remote execution service. This provides “hard isolation,” ensuring the untrusted code runs in a completely separate context from the primary application environment. While offering maximum security separation, this approach typically incurs higher latency and potentially greater cost and complexity compared to local isolation. Technologies like WebAssembly (WASM) sandboxes, potentially including implementations like NVIDIA’s WASM approach or Pyodide within browsers, represent potential mechanisms for achieving this type of isolation. 

Configurability, Visibility, and Analysis

Security within AccuKnox AI-SPM is not one-size-fits-all. The specific parameters for isolation (e.g., allowed network connections, permitted file paths, executable restrictions) are configurable depending on the trust level of the LLM, the sensitivity of the data being processed, and the specific function of the AI application. Crucially, AccuKnox AI-SPM provides detailed visibility into the execution of these sandboxed or isolated jobs. This telemetry feeds into anomaly detection engines that can identify deviations from baseline behavior, potentially indicating a security incident. Furthermore, this data enables comprehensive threat analysis and empirical assessment of agent actions, providing valuable insights for security teams and MLOps engineers.  

Why AccuKnox AI-SPM Stands Out – Our Differentiators

The challenge of securing AI-generated code execution has led to the emergence of various solutions. One notable approach is offered by platforms like E2B, which provide cloud-based infrastructure for running AI-generated code in secure, isolated microVMs that can be spun up quickly. E2B focuses on providing completely isolated sandboxed environments through these microVMs.  

However, this cloud-centric microVM approach, while offering strong isolation, presents certain disadvantages. On-premises deployments can face operational challenges. Auto-generated code might require access to local network endpoints or resources, which can be difficult or impossible to achieve from a remote cloud sandbox. Additionally, the overhead involved in shipping code to a remote environment, executing it, and retrieving the results introduces latency, and the cost associated with dynamically spawning cloud VMs can be significant.  

AccuKnox AI-SPM, particularly with its local process isolation option, offers a compelling alternative designed for seamless integration and efficiency:

Addressing Core Security Gaps

AccuKnox AccuKnox AI-SPM directly tackles the critical security gaps exposed by agentic AI:

AccuKnox AI-SPM stands out by offering lightweight, configurable isolation, minimal operational overhead (especially with local LSMs), and deep integration capabilities. As a sub-product within the AccuKnox Cloud Native Application Protection Platform (CNAPP), it provides a security fabric designed not just for AI, but aware of the specific behaviors and risks introduced by agentic AI.  

Availability

Get started with securing your agentic AI deployments. AccuKnox AI-SPM is currently available in Alpha. Wider access through the Beta program is planned for May 15th, with General Availability (GA) targeted for June 27th. Reach out to the AccuKnox team to explore how AccuKnox AI-SPM can be integrated into your AI/MLOps security strategy. 

You can protect your workloads and achieve runtime security using AccuKnox. AccuKnox CNAPP secures your Kubernetes and other cloud workloads using Kernel Native Primitives such as AppArmor, SELinux, and eBPF.