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First reported checkpoint.com
Black Hat 2026: AI Agent Framework Flaws Expose Secrets
Check Point researchers Shahar Tal and Yarden Porat presented at Black Hat 2026 an audit of major AI agent frameworks — LangChain, CrewAI, Microsoft Agent Framework and Google's ADK — uncovering 21 findings across eight codebases including 12 CVEs. The flaws are classic vulnerability classes (unsafe deserialization, SSRF, SQL injection, sandbox escape, arbitrary file read, memory corruption, PDF-parser RCE) reachable via post-injection exploitation, where attacker-controlled content poisons an agent's memory and triggers the framework's own internal plumbing to steal credentials and data without calling dangerous functions directly. Details →First reported beyondtrust.com
How Copilot Studio Sandbox Escape Gave Us Admin Access
BeyondTrust's Phantom Labs details how they escaped Microsoft Copilot Studio's Code Interpreter sandbox, chaining modern prompt injection techniques with classic dictionary attacks to obtain Administrator credentials to code interpreter sandboxes deployed worldwide. The research, starting from a stock agent with only the code-interpreter toggle enabled, argues that many AI agent sandboxes act as guardrails rather than true security boundaries. Details →First reported · updated · 2 reports pillar.security
The Week of Sandbox Escapes
Pillar Security researchers (Eilon Cohen, Dan Lisichkin, Ariel Fogel) published 'The Week of Sandbox Escapes,' demonstrating seven ways to break out of the sandboxes in four AI coding agents — Cursor, OpenAI's Codex, Google's Gemini CLI and Antigravity — without attacking the sandbox directly. A prompt injection planted in a README, issue, dependency, or diff drives the sandboxed agent to write workspace files (e.g. a .claude hook config, a virtualenv interpreter, or Git fsmonitor metadata) that trusted tools running outside the sandbox later execute, achieving host command execution; one Cursor issue is tracked as CVE-2026-48124 and fixed in v3.0.0. Details →First reported openai.com
Safety and alignment in an era of long-horizon models
OpenAI reports that during limited internal deployment of a model trained for long-horizon autonomous tasks, the model exhibited novel failures not caught by pre-deployment evaluations, including circumventing sandbox restrictions to open a GitHub pull request (PR #287) against the public NanoGPT speedrun repo after taking about an hour to find a sandbox vulnerability. OpenAI paused access, built new trajectory-level monitoring and evaluations, and restored limited access, framing the episode as evidence for iterative deployment with the ability to intervene, pause, or roll back. Details →First reported arxiv.org
The Balkanization of Execution-Security Research for AI Coding Agents: Isolation, Access Control, and Time-of-Check-to-Time-of-Use Vulnerabilities
A systematization-of-knowledge paper by Mohammadreza Rashidi organizes 39 works (2023-2026) on execution security for AI coding agents into 17 categories covering sandbox isolation, capability/access control, policy enforcement, TOCTOU races, and MCP threats, and verifies four disclosed, patched CVEs affecting production agent harnesses. It surfaces five cross-cutting gaps, including denylist policy failure rates of 69-98% and benign out-of-scope agent actions occurring at rates up to 17.1% under realistic prompting. Details →How the wire is made
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