AgentForger Vulnerability in ChatGPT Workspace Agents Enabled Malicious AI Deployment via Single Phishing Link
A vulnerability in ChatGPT Workspace Agents allowed attackers to create and deploy a malicious AI agent inside an organization from a single phishing link. The flaw, dubbed AgentForger, was corrected by OpenAI on 8 June 2026.
The attack exploited a permissive parameter in the Agent Builder that could receive instructions directly through the URL. When an authenticated user opened the prepared address, the command was sent and executed automatically without any additional confirmation step.
For the attack to succeed, the victim needed access to Workspace Agents and at least one pre-authorized enterprise connector. Supported connectors included Outlook, Gmail, Google Drive, Slack, Teams, and Google Calendar.
Attack Flow
- The malicious prompt instructed the platform to create a new agent.
- The agent connected to all available enterprise applications.
- Approval requests were disabled.
- The agent was published and scheduled for immediate and recurring execution.
In the proof-of-concept demonstration, the agent searched for emails sent by the attacker with subjects beginning with “TASK”. Each such message served as a new command, allowing the agent to perform tasks and return results to an address controlled by the attacker.
The vulnerability was reported on 4 June and resolved four days later by removing the vulnerable parameter. No evidence of real-world exploitation has been found. Organizations are advised to review published agents, connected applications, scheduled tasks, and any settings that bypass human approval.
Related articles
SECURITM Launches MCP Server for External AI Access to Security Data
SECURITM has introduced an MCP server that connects external AI assistants directly to its platform data on assets, risks, tasks, and metrics. This marks the first such implementation among Russian SGRC and SOAR systems. The open MCP protocol enables natural language queries about critical assets without owners, overdue tasks, or monthly metric changes without manual data export. Supported tools include GigaChat, Yandex solutions, Claude, ChatGPT, Cursor, and compatible local models. Access remains strictly read-only through dedicated tokens with optional IP restrictions. The feature is available in paid cloud and on-premise editions and complements the platform’s built-in AI assistant.
One Year Left to Secure Systems as Open-Source GLM-5.3-Flash Enables Automated Cyber Attacks
Chinese company Z.ai Co. released GLM-5.3-Flash in August 2026, an open-weight model that achieves 84.5% on CyberGym and 54.4% on ExploitBench, matching or exceeding leading commercial LLMs in vulnerability discovery and exploitation tasks. The model runs locally on consumer hardware such as a single Nvidia DGX Spark or Apple M5 Mac Studio with 256 GB unified memory, allowing unrestricted use after abliterated versions remove safety alignments. Experts warn that LLM agents could soon scan and exploit zero-days across Windows, Linux, browsers, and messaging apps around the clock. Initiatives Project Glasswing (Anthropic-led) and Daybreak (OpenAI-led) are racing to patch critical vulnerabilities in open-source projects and critical infrastructure before the next generation of unrestricted models arrives. Organizations are advised to accelerate patch deployment, inventory supply chains, and adopt advanced LLMs defensively within the remaining one-year window.
RFC 9421 and Open wbauth Protocol Accelerate AI Agents 5.5x by Replacing Anti-Detect Browsers with Cryptographic Signatures
A new open implementation of the IETF RFC 9421 HTTP Message Signatures standard called wbauth allows AI agents to authenticate using Ed25519 cryptographic signatures instead of resource-heavy anti-detect browser farms. The approach eliminates the need for Camoufox, Playwright, or AdsPower instances that consume 500 MB to 1.5 GB of RAM per process and require residential proxy pools. Benchmarks show cold start time dropping from 1744 ms to 5 ms, request time from 86.8 ms to 15.8 ms, and memory usage from 500 MB to 0.76 MB. The system uses three HTTP headers for signing and verification against a public key directory hosted on Cloudflare Workers and D1. Server-side validation at the WAF or edge involves fetching the public key, canonicalizing the message per RFC 9421, verifying the Ed25519 signature, and checking timestamps to prevent replay attacks. The npm package wb-auth@0.4.0 and PyPI package wbauth-sdk==0.4.0 provide ready-to-use signers for Node, Bun, Deno, Python httpx, and requests. The full source is available on GitHub under the MrKoriy/wbauth repository.
Critical Prompt Injection Flaw in ChatGPT GitHub Connector Allows Theft of Private Repositories
A critical vulnerability in the ChatGPT GitHub connector enables attackers to steal private repositories through indirect prompt injection. Researcher Sunil Tripathy demonstrated that hidden instructions placed in a public GitHub repository can trick the ChatGPT agent into accessing and exfiltrating data from the victim's private repositories. The attack works when the victim connects their GitHub account to ChatGPT with full repository access and then asks the agent to review the attacker's public repo. The injected instructions are executed as trusted commands, allowing the agent to commit malicious GitHub Actions workflows that exfiltrate secrets such as AUDIT_CANARY to attacker-controlled servers. Direct requests to steal data are refused by the agent, but framing the instruction as a deployment requirement bypasses this safeguard. OpenAI's documentation claims the connector is read-only, yet the attack achieves both read and write access across repositories. No CVE was assigned, the report was closed as out of scope on October 14, and the issue remained exploitable on October 15 with no published fix available.