Critical Prompt Injection Flaw in ChatGPT GitHub Connector Allows Theft of Private Repositories
A critical vulnerability in the ChatGPT GitHub connector allows attackers to steal private repositories belonging to victims through indirect prompt injection.
Security researcher Sunil Tripathy demonstrated the attack, assigning it a severity score of 9.3. Hidden instructions placed inside a public GitHub repository can cause the ChatGPT agent to reach the victim's private repositories, commit malicious workflows, and exfiltrate stored data.
The technique relies on indirect prompt injection. When the victim asks the agent to review the attacker's public repository, the planted instructions are followed as if they were legitimate user commands. Direct requests to steal data are refused by the agent, but presenting the instruction as a deployment requirement succeeds.
The committed GitHub Actions workflow appears harmless and references an external action containing the exfiltration logic. In testing, a fictional secret named AUDIT_CANARY was sent to a server controlled by the researcher. The agent also located private repositories and wrote attacker-controlled files into them, proving both read and write capabilities.
For the attack to succeed, the victim must have connected GitHub to ChatGPT with access granted to all repositories. The victim does not need to name the target private repository or approve the write operation. The attacker only needs control over the public repository being reviewed.
OpenAI documentation describes the GitHub application as read-only, yet the reported flow includes commits to other repositories. No CVE was assigned. The report was closed as out of scope on October 14, and testing confirmed the attack still worked on October 15. No fix has been published.
The recommended mitigation is to limit the connector's access to only the repositories required for each task. This restriction blocked cross-repository writes in tests. Organizations should also review unexpected workflow commits and rotate any exposed secrets.
Related articles
HackerSec Launches Pentest Superintelligence Using Orchestrated Frontier AI Models
HackerSec has introduced Pentest Superintelligence, a new offensive cybersecurity methodology that orchestrates multiple high-capacity frontier AI models within a single pentest operation. This builds on the company's earlier Pentest AI-First and Pentest AI-Native approaches by requiring at least two frontier models coordinated through a proprietary runtime. The Yaga autonomous agent serves as the core platform, using a custom harness to integrate models from OpenAI, Anthropic, and xAI while preserving operational context from reconnaissance through exploitation. In internal YagaBench evaluations, the six-model orchestrated setup achieved 98 percent resolution of black-box scenarios compared to 40.5 percent for the best single model. All testing remains within authorized scopes with human oversight for result validation. The development shifts industry focus from merely adding AI to tools toward proving effective multi-model coordination and verifiable impact.
Taming the LLM Dragon: Corporate AI Gateway Architecture Addresses Classic Proxy Shortcomings
Spontaneous adoption of generative AI across enterprises has created shadow AI risks, prompt leaks, and uncontrolled token consumption that standard L7 proxies cannot mitigate. Traditional solutions built on Nginx, Envoy or Kong lack the ability to inspect Server-Sent Events streams, classify sensitive content inside prompts, or enforce token-based quotas in real time. The AI Optimum platform introduces a specialized AI gateway layer that sits between internal consumers and both local inference clusters and external APIs such as GigaChat and YandexGPT. It performs inline PII masking, routes traffic according to data sensitivity, supports OpenAI-compatible endpoints, and maintains detailed audit logs required under 152-FZ. The gateway adds only tens of milliseconds of latency while enabling granular billing, context caching optimization, and protection against runaway agent loops. Subsequent articles in the series will examine real corporate AI spend and further regulatory controls.
Why We Banned Our AI Manager from Writing Code and Assigned the Task to Claude Opus
The team behind an AI-powered marketplace manager for Wildberries initially built the system using regular updates, then migrated it to an autonomous agent. After implementing multiple layers of restrictions, they ultimately removed the agent entirely and rebuilt the solution from scratch. The new architecture relies on a single tool that handles the complete Wildberries API along with internet access while respecting all platform limits. The decision to prohibit the original AI manager from generating code stemmed from concerns over reliability and security in production environments. Instead, the developers turned to Claude Opus to handle coding tasks. This case highlights practical challenges of deploying AI agents in e-commerce automation and information security contexts.
Developer Builds MCP Linter and Discovers Security Issues in Anthropic Official Servers
A developer has released mcp-linter, a lightweight security and compliance tool for the Model Context Protocol used by Anthropic's Claude ecosystem. The linter detects critical problems such as stdout pollution that breaks JSON-RPC framing, missing destructiveHint annotations that allow autonomous agents to execute dangerous actions without human confirmation, prompt injection vectors hidden inside tool descriptions, and incomplete input schemas that increase model hallucinations. When run against Anthropic's reference servers, the tool assigned low health scores to server-memory and especially server-filesystem, highlighting dozens of parameters lacking descriptions. The utility runs with zero dependencies, supports SARIF output for GitHub Security, and can generate automatic JSON patches. The project is open source under MIT license and already integrates into CI pipelines via a dedicated GitHub Action.