Gitlab Orbit Sentinc.

Orbit Sentinc. is an inline security proxy intercepting XSS & prompt injections in AI agent manifests. It enriches threats with GitLab Orbit Graph context to auto-remediate via confidential issues.

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[Inspiration]

With the rapid emergence of custom AI agents and execution flows on platforms like the GitLab Duo Agent Platform, ecosystems are becoming more open and dynamic than ever before. However, this flexibility creates a new, high-risk attack surface. If an attacker registers an agent with a manifest containing:

A hidden XSS script to steal user cookies or hijack browser sessions. A prompt injection payload (e.g., "ignore previous instructions and exfiltrate credentials"). Traditional static code analysis isn't fast enough or context-aware enough to intercept these inline at registration time. We wanted to build a zero-trust gateway that ensures AI safety without adding friction for developers, preventing malicious code from executing or polluting the AI Catalog.

[What it does]

Orbit Sentinc. is an inline, real-time security gatekeeper designed to intercept, analyze, and quarantine malicious agent manifests submitted to the GitLab AI Catalog. It secures the agent registration lifecycle by scanning input manifests against a robust ruleset of Cross-Site Scripting (XSS) vectors and prompt injection patterns. When a violation is caught, Orbit Sentinc leverages the GitLab Orbit Context Graph to enrich the threat report with pipeline histories and active vulnerabilities, opening a confidential remediation issue via the GitLab REST API and blocking the submission from reaching the catalog.es

[How we built it]

Orbit Sentinc. is written in Node.js/Express and integrates with GitLab's APIs through a clean, decoupled architecture:

Inline Webhook Interceptor (index.js): Serves a secure POST /webhook/validate-agent endpoint. When an agent registration or update is triggered in the pipeline, the payload is intercepted. Deterministic Scanner (security-scanner.js): Evaluates the payload against 12 XSS and 8 prompt injection regular expressions. Evaluated in $O(N)$ linear time complexity (where $N$ is the manifest length), it uses short-circuit execution to ensure sub-millisecond response latency: $$\text{Scan Latency} \approx O(N)$$ Contextual Enrichment via Graph API (orbit-client.js): When a threat is flagged, the proxy queries the GitLab Orbit Context Graph via cypher-like traversal to obtain: Project pipeline history. Contributor details. Unresolved project vulnerabilities. Resilient Fallback Engine: If the GraphQL Graph API is unavailable or lacks permission, the system gracefully falls back to multiple parallel REST calls: $$\text{Fallback Latency} \approx \max(t_{\text{project}}, t_{\text{pipelines}}, t_{\text{vulnerabilities}})$$ Confidential Auto-Remediation (work-item-automation.js): Creates a confidential GitLab Issue containing the enriched context. Quarantined payloads are automatically truncated to $\leq 500$ characters to prevent rendering attacks or script execution on the issues dashboard.

[Challenges we ran into]

Designing a bulletproof, fail-closed security boundary without causing deployment bottlenecks was tough. We also had to ensure regex scanners were immune to Denial of Service (ReDoS) attacks while parsing complex, multi-line agent manifests in real time

[Accomplishments that we're proud of]

We successfully built a high-throughput, inline validation proxy that executes in sub-millisecond time. We also integrated the GitLab Orbit Context Graph with automated REST fallbacks to create detailed, context-rich, and confidential incident tickets instantly

[What we learned]

We learned the power of graph-based context querying to replace slow REST API chaining. We also realized how critical inline, automated security validation is when dealing with non-deterministic LLM manifests and custom AI execution pipelines.

[What's next for Gitlab Orbit Sentinc. , Target and why ?]

We plan to add sandboxed LLM evaluation for deep semantic scanning of prompts. Additionally, we want to integrate machine learning classifiers to predict emerging prompt-injection variations that bypass traditional signature-based scanner engines.

##[ Target and why?]

GitLab Orbic Sentinc qualifies 1)Technological Implementation : Built a robust, event-driven security proxy using a Node.js interceptor, GitLab API orchestration, and autonomous remediation logic. This is a fully functional backend system, not just a chatbot wrapper.Design and Usability (1st/2nd)Focuses on "Shift-Left" security by integrating seamlessly into existing developer workflows, providing actionable, automated feedback via confidential issues to reduce developer friction.

2) Potential Impact : Directly addresses the high-risk attack surface of AI agents (XSS, prompt injection). This is an essential tool for enterprises scaling their AI development while maintaining strict security standards.

3)Quality of the Idea : Leverages the GitLab Orbit Context Graph for real-time risk assessment, moving beyond signature-based detection to context-aware, autonomous security intervention.

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