GitInject: Real-World Prompt Injection Attacks in AI-Powered CI/CD Pipelines

• URL: http://arxiv.org/abs/2606.09935v1
• Date: Sun, 07 Jun 2026 19:17:18 GMT
• Title: GitInject: Real-World Prompt Injection Attacks in AI-Powered CI/CD Pipelines
• Abstract summary: We present GitInject, an open-source framework for evaluating prompt injection vulnerabilities in real, live GitHub.<n>We study workflow configurations across four AI providers and document eleven named attacks spanning config-file injection, credential exfiltration, judgment manipulation, and availability.<n>We find that all tested providers are susceptible to at least one attack class in their default configuration, and that the most critical vulnerabilities are structural.
• Score: 22.29523291822444
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI-powered agents are increasingly embedded in continuous integration and continuous delivery/deployment (CI/CD) pipelines to autonomously review pull requests (PRs), triage issues, and maintain codebases. These agents ingest untrusted content while operating with elevated repository permissions, making them a natural target for prompt injection attacks with supply chain consequences. We present GitInject, an open-source framework for evaluating prompt injection vulnerabilities in real, live GitHub workflows, a widely deployed instance of CI/CD pipelines. Unlike prior agent security benchmarks that simulate tool calls, GitInject provisions ephemeral repositories and triggers actual workflow runs, so that sandbox constraints, credential handling, and permission boundaries behave exactly as in production. Using GitInject, we study workflow configurations across four AI providers and document eleven named attacks spanning config-file injection, credential exfiltration, judgment manipulation, and availability. We find that all tested providers are susceptible to at least one attack class in their default configuration, and that the most critical vulnerabilities are structural: they arise from how CI/CD infrastructure handles credentials and configuration files, not from any specific model's behavior. For each confirmed attack class, we identify the minimum-cost workflow-level countermeasure and analyze its coverage and limitations. GitInject is released publicly to facilitate further research in this direction.

Related papers

• SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response [38.89333299127352]
  We introduce SecRespond, the first benchmark for evaluating Large Language Model (LLM) agents on the post-compromise incident-response workflow.<n>We instantiate this task across 10 cyber ranges, each constructed from a distinct compromised cloud host.<n> Experimental results show that although current agents can reliably uncover the problems exposed by alerts, they struggle to proactively investigate the disk for silent intrusions.
  arXiv  Detail & Related papers  (2026-07-29T11:32:23Z)
• PhyAgentOS: A Self-Evolving Operating System for Embodied Agents with Decoupled Cognitive Planning and Physical Execution [49.776611937968]
  We present PhyAgentOS, a foundation delivering scheduling, verification, memory, benchmarking, and safety as system-level services.<n>Its Session-Centered treats a session, not an action, as the minimum unit of scheduling, compatibility, supervised execution, evidence collection, and acceptance.<n>SessionVerifier distinguishes execution termination from semantic task completion via evidence-grounded verdicts of success, failure, or replan.<n> Benchmarking reuses the deployment session and verification path, so results trace to real execution.
  arXiv  Detail & Related papers  (2026-07-18T04:46:53Z)
• Comment and Control: Hijacking Agentic Workflows via Context-Grounded Evolution [13.979573641143212]
  GitHub Actions and n8n integrate Large Language Model (LLM) agents for tasks such as code review and data synchronization.<n>An adversary may control and craft certain inputs, such as GitHub issue comments, to manipulate the LLM agent for unwanted actions.<n>In this paper, we design the first detection and exploitation framework, called JAW, to hijack agentic runtime platforms.
  arXiv  Detail & Related papers  (2026-05-11T20:45:31Z)
• ExploitGym: Can AI Agents Turn Security Vulnerabilities into Real Attacks? [92.21756459993695]
  Exploitation is a challenging task because it requires low-level program reasoning.<n>Despite its importance and diagnostic value, exploitation remains under-evaluated.<n>We introduce ExploitGym, a large-scale, diverse, realistic benchmark on the exploitation capabilities of AI agents.
  arXiv  Detail & Related papers  (2026-05-11T18:00:14Z)
• Demystifying and Detecting Agentic Workflow Injection Vulnerabilities in GitHub Actions [13.116427545802571]
  GitHub Actions is increasingly used to deploy LLM-based agents for repository-centric tasks.<n>In this paper, we introduce Agentic Injection (AWI), a workflow-level injection flaw.<n>We characterize 1,033 real-world AI-assisted actions and extract AWI-specific specifications.<n>Based on these specifications, we design TaintAWI, a taint-analysis tool that tracks flows from untrusted event context to agent prompt inputs.
  arXiv  Detail & Related papers  (2026-05-08T02:13:04Z)
• Attesting LLM Pipelines: Enforcing Verifiable Training and Release Claims [2.0403981727850233]
  Modern Large Language Model (LLM) systems are assembled from third-party artifacts such as pre-trained weights, fine-tuning adapters, datasets, dependency packages, and container images.<n>This speed comes with supply-chain risks, including compromised dependencies, malicious hub artifacts, unsafe dedefaultization, forged provenance, and backdoored models.<n>We propose an attestation-aware promotion gate: before an artifact is admitted into trusted environments, the gate verifies claim evidence, enforces safe loading and static scanning policies, and applies secure-by-serial deployment constraints.
  arXiv  Detail & Related papers  (2026-03-30T20:37:48Z)
• AgentWall: A Runtime Safety Layer for Local AI Agents [0.0]
  AgentWall is a runtime safety and observability layer for local AI agents.<n>It intercepts every proposed agent action before it reaches the host environment, evaluates it against an explicit declarative policy, requires human approval for sensitive operations, and records a complete execution trail for audit and replay.<n>We present the design, architecture, threat model, and policy model of AgentWall, and demonstrate 92.9% policy enforcement accuracy with sub-millisecond overhead across 14 benchmark tests.
  arXiv  Detail & Related papers  (2026-03-24T11:39:35Z)
• AEGIS: No Tool Call Left Unchecked -- A Pre-Execution Firewall and Audit Layer for AI Agents [4.963079926145645]
  AEGIS is a pre-execution firewall and audit layer for AI agents.<n>It interposes on the tool-execution path and applies a three-stage pipeline.<n>High-risk calls can be held for human approval, and all decisions are recorded in a tamper-evident audit trail.
  arXiv  Detail & Related papers  (2026-03-13T03:49:12Z)
• SkillJect: Automating Stealthy Skill-Based Prompt Injection for Coding Agents with Trace-Driven Closed-Loop Refinement [120.52289344734415]
  We propose an automated framework for stealthy prompt injection tailored to agent skills.<n>The framework forms a closed loop with three agents: an Attack Agent that synthesizes injection skills under explicit stealth constraints, a Code Agent that executes tasks using the injected skills and an Evaluate Agent that logs action traces.<n>Our method consistently achieves high attack success rates under realistic settings.
  arXiv  Detail & Related papers  (2026-02-15T16:09:48Z)
• Trace: Securing Smart Contract Repository Against Access Control Vulnerability [58.02691083789239]
  GitHub hosts numerous smart contract repositories containing source code, documentation, and configuration files.<n>Third-party developers often reference, reuse, or fork code from these repositories during custom development.<n>Existing tools for detecting smart contract vulnerabilities are limited in their ability to handle complex repositories.
  arXiv  Detail & Related papers  (2025-10-22T05:18:28Z)
• Cuckoo Attack: Stealthy and Persistent Attacks Against AI-IDE [64.47951172662745]
  Cuckoo Attack is a novel attack that achieves stealthy and persistent command execution by embedding malicious payloads into configuration files.<n>We formalize our attack paradigm into two stages, including initial infection and persistence.<n>We contribute seven actionable checkpoints for vendors to evaluate their product security.
  arXiv  Detail & Related papers  (2025-09-19T04:10:52Z)
• DRIFT: Dynamic Rule-Based Defense with Injection Isolation for Securing LLM Agents [52.92354372596197]
  Large Language Models (LLMs) are increasingly central to agentic systems due to their strong reasoning and planning capabilities.<n>This interaction also introduces the risk of prompt injection attacks, where malicious inputs from external sources can mislead the agent's behavior.<n>We propose a Dynamic Rule-based Isolation Framework for Trustworthy agentic systems, which enforces both control and data-level constraints.
  arXiv  Detail & Related papers  (2025-06-13T05:01:09Z)
• VPI-Bench: Visual Prompt Injection Attacks for Computer-Use Agents [74.6761188527948]
  Computer-Use Agents (CUAs) with full system access pose significant security and privacy risks.<n>We investigate Visual Prompt Injection (VPI) attacks, where malicious instructions are visually embedded within rendered user interfaces.<n>Our empirical study shows that current CUAs and BUAs can be deceived at rates of up to 51% and 100%, respectively, on certain platforms.
  arXiv  Detail & Related papers  (2025-06-03T05:21:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.