CARI: Cross-Boundary Adversarial Runtime Injection Against Coding Agent Workflows
CARI: Cross-Boundary Adversarial Runtime Injection in Coding-Agent Workflows
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Researchers propose CARI, a cross-boundary adversarial runtime injection attack against coding agent workflows: the attacker controls an active component of another role in collaborative development (such as a backend service, test framework, or CI job), and embeds malicious instructions in the runtime feedback that the victim agent needs to complete its task, inducing it to perform unauthorized operations. The paper constructs ten attacks based on MITRE ATT&CK, covering resource exhaustion, information exposure, and tampering with development state. In 3000 trials across implementations in four languages (Python, Go, Java, Rust) and five code models, the attacks succeeded 2845 times (94.8%), and can transfer to product-level agents, with success rates of 61.3% on Codex and 58.7% on Claude Code. Controlled experiments show that when a byte-identical payload is delivered as diagnostic feedback, the success rate is far higher than with static documents or passive logs: GPT-5.4-mini rises from 8.9% to 93.3%, and Claude Sonnet 4.5 from 2.2% to 97.8%.
- In collaborative coding agent workflows, different developer roles each control components, but the agent converts runtime feedback returned by the counterpart's components (API responses, test failures, CI diagnostics, etc.) into concrete tool operations. The authors point out that the counterpart's components only have diagnostic permissions and no permission to operate the victim's local files/credentials/shell, and this permission mismatch constitutes a new attack surface.
- The authors propose CARI (Cross-Boundary Adversarial Runtime Injection): an attacker controls a counterpart development component and embeds malicious instructions in runtime feedback triggered by the victim's legitimate interactions, inducing the agent to perform sensitive operations across boundaries. They construct ten attack instances following MITRE ATT&CK, covering resource exhaustion, information leakage and development state tampering, and evaluate them on four implementations (Python/Go/Java/Rust) and five models.
- It succeeded 2,845 times out of 3,000 trials (94.8%), with little variation across languages; the same malicious text as action-triggering diagnostic feedback had a much higher success rate than as a static document or passive log. After transferring to Codex and Claude Code, the success rates were 61.3% and 58.7%, respectively, and rewording could restore part of the suppressed attacks. Workflow-Level DoS can cause about 2,000× token amplification, and the authors demonstrate re-delivery, state-conditioned triggering, downstream re-implantation, and multi-victim propagation.
- The evaluation is based on a controlled Todo application, and the authors do not claim that success rates remain unchanged in production environments. Prompt-level defenses involve a security-utility tradeoff: generic warnings are almost ineffective, while explicit policies substantially reduce attacks but severely over-refuse legitimate tasks. The authors propose binding the source of runtime feedback to its role's authorization scope and mediating sensitive operations at execution time, but they did not implement or evaluate this.
The paper uses 3000 trials to quantify the success rate of runtime feedback injection on five models and product-level Agents, and demonstrates the security-usability trade-off of prompt-level defenses.