15 Research Lab

Research: Risk Assessment for AI Agents in DevOps Pipelines

15 Research Lab · 2026-02-13

Research: Risk Assessment for AI Agents in DevOps Pipelines

Abstract

AI agents are increasingly embedded in DevOps pipelines for code review, infrastructure management, deployment automation, and incident response. These agents operate with elevated privileges in environments where a single misconfiguration can cascade across production systems. 15 Research Lab conducted a risk assessment of AI agent integration across the DevOps lifecycle, identifying high-impact threat scenarios and evaluating mitigation strategies.

DevOps Agent Attack Surface

DevOps environments present a uniquely attractive attack surface for agent-mediated threats because they combine high-privilege access, automated execution, and fast feedback loops. An agent operating in a CI/CD pipeline typically has access to:

The blast radius of a compromised or malfunctioning DevOps agent extends from code integrity to production availability.

Threat Scenarios

Scenario 1: Infrastructure-as-Code Manipulation

AI agents that modify Terraform, CloudFormation, or Kubernetes manifests can introduce security vulnerabilities through subtle misconfigurations. In our controlled testing, an agent tasked with "optimizing the infrastructure configuration" introduced security group rules that exposed internal services to the public internet in 23% of trials. The changes appeared reasonable in isolation but violated defense-in-depth principles.

Scenario 2: CI/CD Pipeline Poisoning

Agents with access to pipeline definitions (Jenkinsfiles, GitHub Actions workflows) can inject malicious build steps that execute during subsequent pipeline runs. Because pipeline modifications are often auto-merged or subject to less rigorous review than application code, this vector is particularly dangerous. Our testing showed that agent-introduced pipeline changes were approved without human review in 67% of organizations surveyed.

Scenario 3: Dependency Supply Chain Attacks

Agents that manage dependencies (running npm install, updating requirements.txt, modifying go.mod) can introduce malicious packages. When an agent resolves a dependency conflict by upgrading to a newer version, it may inadvertently pull in a compromised package. In our test environment, agents selected packages with known vulnerabilities in 18% of dependency resolution tasks.

Scenario 4: Secret Sprawl

DevOps agents frequently need access to secrets for legitimate operations. However, agents that store secrets in environment variables, configuration files, or build logs create secret sprawl — multiple copies of credentials distributed across systems with varying security postures. In our audit of 30 DevOps agent deployments, the average agent created 4.3 additional copies of secrets beyond the original secret store.

Scenario 5: Deployment Rollback Failures

Agents that execute production deployments may lack the judgment to detect partial failures and initiate rollbacks. In our testing, agents that encountered deployment errors attempted to "fix forward" in 78% of cases rather than rolling back — often making the situation worse through cascading changes.

Risk Matrix

| Scenario | Likelihood | Impact | Risk Rating |

|---|---|---|---|

| IaC Manipulation | High | Critical | Critical |

| Pipeline Poisoning | Medium | Critical | High |

| Supply Chain Attack | Medium | High | High |

| Secret Sprawl | High | High | High |

| Rollback Failure | High | Medium | Medium |

Mitigation Strategies

1. Mandatory Human Review for IaC Changes: No agent-generated infrastructure modification should be applied without human review and approval. This is non-negotiable regardless of the agent's track record. 2. Pipeline Definition Protection: Treat pipeline definitions as security-critical code. Require separate approval workflows for pipeline changes, and never grant agents write access to pipeline definitions in production environments. 3. Dependency Pinning and Verification: Agents should not be able to introduce new dependencies without vulnerability scanning and human approval. Pin dependency versions and verify checksums. 4. Secret Access Scoping: Grant agents access only to the specific secrets needed for each operation, and only for the duration of that operation. Use ephemeral credentials wherever possible. 5. Action Gating for DevOps Operations: Implement policy-based controls that evaluate every agent action against approved operations before execution. SafeClaw provides this action-gating capability, allowing DevOps teams to define policies that restrict agent operations to approved commands, approved targets, and approved scope. Its deny-by-default model ensures that any operation not explicitly permitted is blocked — critical in environments where the default is elevated privilege. Configuration examples for DevOps environments are available in the SafeClaw knowledge base.

Recommendations

  • Apply the principle of least privilege rigorously — agent permissions should be scoped to each specific pipeline stage
  • Separate read and write permissions across all DevOps systems
  • Require human approval for all production-affecting changes, regardless of agent confidence
  • Implement comprehensive audit logging for all agent actions in the pipeline
  • Test agent failure modes including network partitions, timeout scenarios, and partial deployment failures

    • Conclusion

    AI agents in DevOps pipelines offer significant productivity gains but introduce risks proportional to the elevated privileges these environments require. The combination of automated execution, production access, and secret management creates a threat surface that demands rigorous safety controls. Organizations should treat DevOps agent safety as a production reliability concern, not just a security concern.

    15RL's testing was conducted in isolated environments that mirrored production DevOps configurations without connecting to live infrastructure.