CVE-2026-33068 is a configuration loading order defect in Anthropic's Claude Code CLI tool (versions prior to 2.1.53). A malicious `.claude/settings.json` file in a repository can bypass the workspace trust confirmation dialog by exploiting the order in which settings are resolved. The mechanism: Claude Code supports a `bypassPermissions` field in settings files. This is a legitimate, documented feature intended for trusted workspaces. The vulnerability is that repository-level settings ( `.claude/settings.json` ) are loaded and resolved before the workspace trust dialog is presented to the user. A malicious repository can include a settings file with `bypassPermissions` entries, and those permissions are applied before the user has an opportunity to review and approve the workspace. This is CWE-807: Reliance on Untrusted Inputs in a Security Decision. The trust decision (whether to grant elevated permissions) depends on inputs from the entity being evaluated (the repository). The security boundary between "untrusted repository" and "trusted workspace" is bridged by the settings loading order. The fix in Claude Code 2.1.53 changes the loading order so that the trust dialog is presented before repository-level settings are resolved. Worth noting: `bypassPermissions` is not a hidden feature or a misconfiguration. It is documented and useful for legitimate workflows. The bug is purely in the loading order. 

Compiled over the past few weeks. Covers four streams: Adversarial ML, Agent Security, Supply Chain, and Prompt Injection. Highlights by severity: **CRITICAL (9 advisories)** - ML model scanner universal blocklist bypass -- the scanner HuggingFace Hub relies on for model upload safety can be completely bypassed via stdlib modules. CVSS 10.0. - Flowise 6-vuln cluster (CVE-2026-30820 through CVE-2026-31829) -- missing auth, file upload, IDOR, mass assignment, SSRF. CVSS 9.8. - MLflow auth bypass chained to RCE via artifact path traversal (CVE-2026-2635 + CVE-2026-2033). Default install ships with hardcoded credentials. CVSS 9.8. - vLLM RCE via video processing pipeline (CVE-2026-22778) -- heap overflow to ASLR bypass, unauthenticated. CVSS 9.8. - Agenta LLMOps sandbox escape + SSTI (CVE-2026-27952, CVE-2026-27961). CVSS 9.9. - claude-code-ui triple command injection (CVE-2026-31975, CVE-2026-31862, CVE-2026-31861). CVSS 9.8. **Notable HIGH/MEDIUM** - LangGraph checkpoint unsafe msgpack deserialization (CVE-2026-28277) + Redis query injection (CVE-2026-27022) - PyTorch weights_only unpickler memory corruption (CVE-2026-24747) -- defeats the mitigation everyone recommends - MCP server vulnerabilities across mcp-server-git, mcp-atlassian, WeKnora - First documented in-the-wild indirect prompt injection against production AI agents (Unit 42 research) Each advisory includes full attack chain analysis, MITRE ATLAS mapping where applicable, and Sigma detection rules you can deploy. 94 rules total across the 22 advisories. 

found this during a routine supply chain audit of our own codebase. the part that concerns us most is the false patch problem - anyone who responded to CVE-2025-58367 last year updated the restricted unpickler and considered that attack surface closed. it wasn't. if you're running the likes of SageMaker, DataHub, or acryl-datahub and haven't pinned to 8.6.2 yet, worth checking now.

How AI helped us in the process of finding an Unauthenticated PHP Object Injection in a WordPress plugin. In this blog post, we discuss how we discovered and exploited the vulnerability using a novel POP chain.

Not sure if I'm the only one but I've always thought looking up CVEs felt archaic and outdated. I'm also a visual learner so I always wished there was some kind of visual graph that explains the E2E attack chain for me.

So rather than complaining, I built VulnPath as a fun side project. It's a CVE visualization tool where it will not only give you the full CVE data, but also a node graph visualizing the attack chain. I also added a "Simple" toggle for situations where you may need to explain the vulnerability to a less technical audience.

I honestly just want to know if this is something other people would find useful, or if I'm solving a problem that only bothers me. Please feel free to check it out; any feedback/suggestions are welcome (including if you think this is a terrible idea lol).

Note: the webapp isn't really mobile friendly (for now), so apologies in advance!

As usual, in-depth sample analysis on linked files