The Top Code Execution Risks in Agentic AI Systems in 2026

Key takeaways

• Agentic AI introduces continuous code execution paths that operate outside traditional development and review workflows, expanding execution risk inside trusted systems.
• Code execution vulnerabilities increasingly stem from how agents interpret context, select tools, and act autonomously, not from developer-written logic alone.
• Managing AI code execution risk requires architectural visibility and execution-aware controls that span design, code, and runtime.

Code execution was once tightly controlled by developers, pipelines, and review gates. 

Now, agentic AI is changing that model by generating and running code dynamically, often with broad permissions and limited visibility into how decisions are made or actions are triggered.

This shift is already happening. According to Gartner, 33% of enterprise software applications will use agentic AI by 2028, with roughly 15% of day-to-day work decisions made autonomously. That autonomy is what makes agentic systems powerful, but it also reshapes where execution risk lives.

In traditional software, code execution is deliberate and traceable. A developer writes code, a pipeline builds it, and controls are applied before anything reaches production. Agentic AI compresses that lifecycle as agents interpret intent, generate logic at runtime, select tools, and execute code as part of normal operation. 

This creates a growing attack surface for code-execution vulnerabilities that emerge after development and outside standard review processes.

Understanding and managing that risk requires a different way of thinking about application security, one grounded in architecture, execution context, and continuous visibility.

Why Agentic AI Changes the Code Execution Risk Landscape

Agentic AI introduces a new execution model where software behavior is determined at runtime rather than defined entirely during development. 

These systems plan actions, generate logic, and execute code dynamically based on context and available tools. That shift changes where execution risk originates, how it propagates, and why traditional application security controls struggle to contain it.

Code execution becomes a runtime capability, not a deployment event

Agentic AI systems do not wait for developers to ship code before execution happens. Agents observe context, generate logic, and execute code as part of normal operation. Execution is continuous and happens inside production workflows rather than at the end of a controlled pipeline.

This changes how execution risk manifests. Instead of known code paths reviewed before release, execution decisions are made dynamically, often without persistent artifacts or traditional checkpoints.

Inputs turn into executable behavior

In agentic systems, inputs extend far beyond direct user prompts. Agents consume documents, logs, API responses, memory entries, and tool outputs. That information influences planning and action selection, which can result in code being generated and run in response to data that was never intended to drive execution.

As a result, the boundary between data and instructions becomes unstable. Execution risk emerges from how context is interpreted, not just from what developers explicitly wrote.

Permission scope amplifies execution impact

Agents are typically granted broad service-level permissions to complete tasks across systems. That access is operationally necessary, but it also raises the stakes of any code execution vulnerability.

When execution happens with legitimate credentials, the resulting actions look authorized. That makes misuse harder to detect and increases the potential blast radius of a single compromised execution path.

Traditional AppSec assumptions no longer hold

Most application security tooling assumes a clear separation between development, deployment, and runtime. Agentic AI compresses those phases into a single loop. Code is generated and executed on demand, often without static visibility or clear ownership.

Assessing risk in this model requires understanding how agents reason, what tools they can invoke, and how execution authority is granted. This is why agentic AI vulnerability assessments must focus on autonomy, execution control, and architectural exposure rather than prompt behavior alone.

Key Characteristics of Agentic AI Systems

Agentic AI systems share a set of architectural and operational traits that distinguish them from traditional applications and earlier generative AI tools. 

These characteristics explain why AI code execution behaves differently and why execution risk accumulates outside familiar control points.

• Autonomous planning and self-directed execution: Agents decompose goals into steps, sequence actions, retry failures, and adapt plans without human involvement. Execution decisions are made at runtime based on context rather than predefined workflows, introducing execution paths that do not exist during development or review.
• Dynamic code generation and execution: Many agentic systems generate and execute code on the fly to complete tasks such as automation, data processing, or system interaction. This code is often transient and never stored as a persistent artifact, making application security posture management more difficult as execution behavior shifts continuously.
• Tool and interpreter access: Agents commonly have access to code interpreters, shell environments, package managers, cloud APIs, and internal services. These tools expand capability but make it hard to prevent malicious code when untrusted context influences how tools are invoked.
• Broad permission scope: To operate effectively, agents often run with service-level credentials rather than user-scoped permissions. That access allows them to act across systems and environments, amplifying the impact of any code execution vulnerability that emerges during autonomous operation.
• Persistent context and memory: Agentic architectures frequently include short-term and long-term memory that persists across sessions. Unsafe instructions or behaviors can accumulate over time, influencing future execution decisions even when no single interaction appears dangerous.

Understanding these characteristics is essential for evaluating execution risk, since failures emerge from how autonomy, context, and execution authority interact across the system.

5 Code Execution Risks Impacting Agentic AI Systems

Agentic AI expands code execution beyond traditional development boundaries. Execution risk now emerges from how agents interpret context, chain actions, and operate autonomously inside trusted environments.

Risk 1: Indirect prompt injection that drives executable behavior

Indirect prompt injection occurs when untrusted data is absorbed into an agent’s context and treated as guidance for action. This data may come from documents, logs, tickets, APIs, or retrieved memory rather than direct user input.

How it leads to code execution

When injected context influences planning, an agent may generate code, invoke tools, or execute commands as part of completing a task. If interpreters or automation frameworks are available, execution follows naturally from reasoning rather than from an explicit exploit sequence.

Why traditional controls fail

Injected content often resembles legitimate business data and flows through trusted ingestion paths. Static analysis does not inspect runtime context, and runtime monitoring typically records only authorized actions performed with valid credentials.

Why agentic AI makes this worse

Agentic systems persist and iterate. Injected instructions can be reused across sessions or workflows, increasing the likelihood that a code execution vulnerability surfaces during normal operation rather than as an isolated event.

Risk 2: AI-driven supply chain compromise through autonomous dependency execution

Agentic systems frequently generate code that depends on external libraries or infrastructure components selected at runtime rather than from approved dependency lists.

How it leads to code execution

Installing or importing dependencies often triggers immediate execution through install scripts, initialization logic, or runtime imports. If an agent selects a malicious or poisoned dependency, execution occurs inside a trusted environment without a separate exploit step.

Why traditional controls fail

Most supply chain protections assume human-driven workflows where dependencies appear in pull requests and are scanned before use. Autonomous dependency selection bypasses those checkpoints, and transient or newly published packages often evade detection.

Even when scanning tools are in place, they struggle to evaluate transient or newly published packages. This creates blind spots that are well documented in modern SCA vulnerabilities, where detection lags behind execution and impact.

Why agentic AI makes this worse

Agents optimize for task completion. They may reuse previously successful dependencies and propagate unsafe selections across workflows, compounding exposure over time without human review. 

Because dependency execution is tightly coupled to autonomy, this risk sits squarely within the domain of software supply chain risk management. The challenge is no longer just identifying vulnerable components, but understanding how and when agents introduce new execution paths into the supply chain without human oversight.

Risk 3: Privilege escalation and confused-deputy failures in agentic execution paths

Agentic AI systems act as intermediaries between users, services, and infrastructure, executing actions using their own credentials.

How it leads to code execution

If an attacker influences an agent’s decision-making, the agent may generate scripts or invoke automation that exceeds the original intent of the request. In these scenarios, the agent becomes the vehicle for a remote code execution attack despite using legitimate credentials.

Why traditional controls fail

Access controls focus on who is authorized, not how execution decisions are derived. Logging captures actions but rarely records the reasoning behind them, making misuse difficult to distinguish from normal automation.

Why agentic AI makes this worse

Agentic systems are designed to connect systems and translate intent into action. That connective role increases the risk that execution authority is misapplied as agents operate continuously and adapt behavior over time. 

This challenge is increasingly discussed in the context of generative AI security for application security teams, where the focus shifts from static permissions to how execution decisions are made and enforced.

Risk 4: Memory poisoning and delayed execution across agent workflows

Agentic systems rely on memory to maintain context and improve efficiency across sessions. Unsafe instructions or behaviors can persist long after the original interaction.

How it leads to code execution

Poisoned memory can influence future planning and tool selection. When agents later generate scripts or automation, execution may be driven by stored context rather than current intent.

Why traditional controls fail

Memory-driven behavior breaks request-level inspection models. The data influencing execution may have been ingested long before execution occurs, leaving no clear causal trail.

Why agentic AI makes this worse

Agents reinforce successful behavior over time. Once unsafe execution patterns enter memory, they can influence many workflows, increasing systemic risk.

Risk 5: Unsafe sandboxing and execution environment assumptions

Agentic AI systems often rely on sandboxes or containers to limit the impact of executing generated code.

How it leads to code execution

Sandboxes contain execution but do not eliminate risk. Misconfigurations, shared kernels, or permissive runtime permissions can allow executed code to affect adjacent services or the host.

Why traditional controls fail

Sandboxing is often treated as a binary safeguard. In reality, isolation strength varies, and monitoring typically focuses on inputs and outputs rather than boundary behavior.

Why agentic AI makes this worse

Agents execute code frequently and persistently. Over time, repeated execution increases the likelihood that sandbox weaknesses are exercised, turning theoretical gaps into real failures.

Securing Code Execution in the Age of Autonomous Software

Agentic AI changes how software behaves. Code execution is no longer confined to reviewed commits and controlled pipelines. It happens dynamically inside trusted systems, driven by agents that plan, adapt, and act with broad authority.

Managing this shift requires continuous visibility into software architecture, execution paths, and material changes across the SDLC. Security teams need to understand not just what code runs, but why it runs and how execution authority is applied.

Today’s security teams need continuous visibility into how code, dependencies, and execution paths evolve across the SDLC. They need to understand which changes introduce risk, how execution decisions are made, and where autonomy intersects with production access.

Apiiro provides that foundation by automatically mapping software architecture, tracking material changes, and connecting code to runtime context. With deep code analysis, risk-aware prioritization, and agent-driven remediation, Apiiro helps teams prevent code execution risks before they reach production.

Book a demo to see how Apiiro secures agent-driven software without slowing innovation.

FAQs

How does remote code execution differ when triggered by an AI agent?

In agentic systems, execution is initiated by autonomous decision-making rather than a direct exploit. The agent uses legitimate tools and credentials, making execution harder to distinguish from normal behavior.

What role does sandboxing play in securing autonomous agent actions?

Sandboxing limits blast radius but does not eliminate risk. Weak isolation, shared resources, and permissive configurations can still allow executed code to impact surrounding systems.

Can AI agents unintentionally escalate privileges during code execution?

Yes. Agents often operate with service-level permissions, which can be misapplied when execution decisions are influenced by untrusted context or indirect inputs.

How should organizations audit their AI pipelines for code execution risks?

Audits should focus on execution authority, material changes to software architecture, dependency introduction, and how agents select and invoke tools over time.