eBPF Security: What Kernel-Level Visibility Catches, and What It Cannot

eBPF has become an important building block for Linux runtime security because it can observe behavior close to the Linux kernel without changes to every application. Security teams can use it to monitor processes, files, network connections, and system calls across hosts and containers.

But kernel-level visibility has a boundary. In an AI agent workflow, for instance, it can show the resulting system-level activity without automatically explaining which application request caused it, which authenticated identity initiated the workflow, why the agent chose that action, or what business outcome followed. That distinction matters as enterprises deploy custom-built AI agents into production workflows.

What Is eBPF Security?

Extended Berkeley Packet Filter, or eBPF, allows verified programs to run at defined hooks in the Linux kernel and related interfaces without requiring a traditional loadable kernel module. Security tools can use these hooks to observe system calls, process creation, file access, network activity, and other operating system events without modifying application source code.

This architecture provides broad, low-level telemetry across heterogeneous workloads. eBPF programs are constrained by a verifier before they run, although recent USENIX Security 2025 research shows that verifier complexity itself remains an active security engineering concern.

What eBPF Catches Well

eBPF is strongest when the security issue is reflected in operating system behavior. It gives defenders a detailed view of activities that cross the kernel boundary across different application stacks.

1. Syscall anomalies: eBPF-based security tools can observe unusual file access, execution attempts, permission changes, and other kernel-mediated activity. These signals can help surface behaviors associated with privilege escalation, container escapes, and access to protected resources.
2. Network threats: eBPF networking hooks can reveal unexpected outbound connections, unusual east-west communication, lateral movement, and potential exfiltration paths. This is useful when the connection itself is the important indicator.
3. Process-Level Threats: Process telemetry can expose suspicious shells, unexpected child processes, and unusual execution chains. If an application compromise leads to command execution on a host or inside a container, eBPF can provide valuable evidence of what ran.
4. Zero-Touch Deployment Across Languages and Runtimes: eBPF can collect kernel-level telemetry without requiring teams to instrument each application’s source code. The security product still needs a deployment mechanism and compatible kernel capabilities, so “zero touch” means no application code modification rather than no deployment work.

Limitations of eBPF Security

Seeing activity at the kernel level does not mean understanding what the application is doing or why. A syscall can reveal that a process opened a file, connected to an endpoint, or launched a child process, but it does not inherently carry the business and application semantics that caused the action.

A single service process may handle many users, requests, and agent workflows. Asynchronous runtimes can separate the initiating request from the operation it triggers. Encryption can hide application payloads from network observation, while JIT compilation, shared workers, and framework abstractions can complicate code-level attribution.

eBPF also operates within kernel and verifier constraints. Programs must pass verification, supported hooks vary by environment, and security products typically require privileged components to load or manage probes. These tradeoffs reinforce that eBPF is a specialized visibility layer, not a complete reconstruction of application intent.

eBPF vs. Agent-Based vs. Application Layer Runtime

These approaches observe different layers of execution. They can overlap technically, and an eBPF product may still use a user-space daemon, but their primary visibility points and blind spots are different.

The Semantic Gap: What eBPF Cannot See at the Application Layer

The semantic gap appears when low-level behavior looks normal, but its meaning changes with application context. Kernel telemetry can prove that a process performed an operation, but it cannot automatically determine whether that operation was authorized by the right user, followed the intended workflow, or produced an acceptable business outcome.

Hijacked and Legitimate Agents Look the Same

Consider an enterprise payment agent that legitimately calls a payment API. A prompt injection attack could manipulate that same agent into calling the same API with an attacker-controlled destination. At the kernel level, both flows may use the same process, socket, TLS connection, file descriptors, and system calls.

The difference is semantic. One execution follows the intended business workflow, while the other abuses legitimate application capabilities. Recent research on eBPF-based AI agent observability describes this as a semantic gap between high-level agent intent and low-level system activity.

What Kernel Visibility Cannot Reconstruct

A kernel event does not inherently identify the originating HTTP request, application endpoint, exact function, authenticated user, agent goal, or business transaction. Some details can be approximated through user-space probes, symbols, tracing, and correlation, but they are not native properties of the syscall.

That matters during an investigation. Knowing that a process wrote to a database helps. But knowing that a claims agent approved a specific claim, which user triggered it, which function authorized it, and whether the approval violated policy answers a different security question.

Why the Gap Widens With Custom-Built AI Agents

Custom-built AI agents introduce autonomy and probabilistic decision-making into production applications. The relevant security unit is no longer only a process or request, but the execution chain from user intent through model interactions, application code, tool calls, data access, and business effect.

Non-Deterministic Execution: Same Input, Different Code Paths

Traditional applications usually map inputs to relatively predictable execution paths. AI agents can select different tools, steps, or downstream actions from similar inputs because model outputs and orchestration decisions are probabilistic.

The same low-level event can therefore have different meanings across executions. An outbound API request may be expected in one workflow and evidence of goal drift in another. Security analysis needs the surrounding execution context to distinguish them.

Prompt Injection Produces Normal-Looking Syscalls

Prompt injection does not need to exploit the kernel. It can steer a trusted agent into using legitimate tools, credentials, and application functions for an unintended purpose.

The OWASP Top 10 for Agentic Applications 2026 highlights agent goal hijack, tool misuse, and identity and privilege abuse as distinct agentic risks. In these scenarios, the operating system may see authorized processes performing technically normal operations. The security problem lies in why the agent selected those operations and whether they match the intended task and policy.

Why an MCP Gateway Does Not Close It Either

An MCP gateway can centralize controls and visibility for MCP traffic. But a custom-built agent workflow can also include direct API calls, internal services, databases, application functions, memory, model calls, and custom tools outside MCP.

An MCP gateway alone is therefore not sufficient to secure the complete agent workflow. It governs one interaction boundary. Application-layer security is still needed when the security decision depends on the full execution chain and business context.

Where eBPF, Gateways, and Application-Layer Security Each Fit

These controls address different parts of the stack, so enterprises may use more than one. eBPF is well suited to infrastructure, Kubernetes, process, and network visibility; gateways can control traffic they mediate; and application-layer security addresses the execution semantics of custom-built applications and agents.

The distinction also depends on the kind of agent being protected. Productivity agents primarily help employees write, research, summarize, or perform internal tasks. Enterprise agents are production systems embedded in business-critical workflows, with access to customer data, money, claims, fulfillment, or other consequential processes. Those enterprise agents require security that connects technical actions to application intent and business outcomes.

How to Evaluate Runtime Security for AI Application Security

Runtime security for AI Application Security should be evaluated by the decisions its telemetry can support, not only by how much telemetry it produces. For enterprise agents, the strongest evidence connects an observed action to the application context that made the action meaningful.

1. Can it name the function, endpoint, and identity behind an action? A useful runtime view should attribute behavior to the relevant application component and initiating identity, not stop at a PID, socket, or syscall.
2. Can it connect that action to a business outcome? The same technical action can be acceptable in one workflow and harmful in another. Security teams need enough context to distinguish a normal refund from an unauthorized one, or an approved claim from a manipulated approval.
3. Can it enforce agent action constraints as the action executes? Visibility explains what happened. Runtime protection should also determine whether an action violates policy while the execution context is still available.
4. Does execution data leave the organization? Agent traces can contain prompts, customer data, tool parameters, code context, and sensitive business information. Runtime security should not create a new data exposure path while protecting the application.

How Rein Adds Application Context That eBPF Cannot See

Rein is security purpose-built for enterprise agents. It uses a code-native sidecar that sees the agent’s application layer runtime, giving Rein in-process, execution-level visibility without relying on eBPF, a gateway, or a proxy to reconstruct what the application is doing.

Its approach is built around four pillars:

• Visibility: Rein captures the complete execution chain – every prompt, service call, tool invocation, and resource touched – and connects those actions to their business outcomes. This is in-process, application-layer visibility that kernel and gateway tools cannot replicate.
• Prevention: Rein enforces contextual action-level controls throughout the execution flow, automatically baselining normal agent behavior and stopping deviations in real time without taking the agent down.
• Posture: The same Rein deployment covers code security, vulnerability management, compliance, supply chain security, and governance for agents, extending to MCP protection, SCA and reachability, AI-powered SAST, API security, and detection and response under one roof.
• Privacy: Every byte of execution data stays inside the organization. There is no gateway or vendor infrastructure in the data path, so sensitive runtime context does not leave the perimeter.

This positions Rein within AI Application Security for Custom-Built AI, specifically AI Agent Security. It addresses a different security question from eBPF: what did the enterprise agent do inside the application, why did it happen, who caused it, and what did it mean to the business?

Conclusion

eBPF security is powerful when the security question lives at the kernel, process, container, or network layer. It can expose suspicious behavior across heterogeneous workloads without requiring application code changes and provides valuable infrastructure evidence during detection and investigation.

Its boundary becomes clear when security depends on meaning rather than activity alone. Enterprise agents can cause harmful outcomes through legitimate processes, syscalls, credentials, and network connections. Securing those workflows requires application-layer execution context that connects identity, code path, agent action, and business outcome.

Enterprise agents are part of the business itself. Rein is security purpose-built for enterprise agents, with the execution-level context and control needed to protect actions that can directly affect customers, data, money, and operations.