#security + #code

The OWASP GenAI Security Project is a global, open-source initiative dedicated to identifying, mitigating, and documenting security and safety risks associated with generative AI technologies, including large language models (LLMs), agentic AI systems, and AI-driven applications.

Three versions of the durabletask PyPI package (1.4.1, 1.4.2, 1.4.3), Microsoft’s Durable Task SDK for Python, were published on May 19, 2026 using a compromised PyPI API token.

The dropper downloads a stage-2 Python zipapp (rope.pyz) from attacker infrastructure and executes it with all output suppressed. The stage-2 is a full credential harvesting framework with dedicated collectors for AWS Secrets Manager and SSM Parameter Store, Azure Key Vault, GCP Secret Manager, Kubernetes secrets (across all contexts), HashiCorp Vault, and local password managers (1Password, Bitwarden, pass, gopass). It also reads over 90 sensitive files from disk, exfiltrates everything encrypted with RSA-4096/AES-256-GCM to a C2 server, and propagates itself to other hosts via AWS SSM SendCommand and kubectl exec.

The payload includes geopolitical targeting: it skips systems with a Russian locale and contains a destructive rm -rf /* routine targeting Israeli and Iranian systems.

Password Managers (collectors/passwords.py): Attempts to unlock 1Password, Bitwarden, pass, and gopass by brute-forcing passwords harvested from environment variables matching PASS, SECRET, KEY, BW_, OP_, _MASTER patterns, and from shell history (.bash_history, .zsh_history). On success, it dumps every item from every vault.

Filesystem (collectors/filesystem.py): Reads 90+ files including SSH keys, cloud credentials, Docker configs, npm/PyPI/Cargo/Gem tokens, kubeconfig, Terraform state files, VPN configurations (Tailscale state, WireGuard configs), MCP server configs (Claude Desktop, Cursor, VS Code, Zed, Codeium, Continue), and all .env files found under the home directory. Also extracts environment variables from all Docker containers via the Docker socket or CLI, and collects GitHub tokens via gh auth token.

and collects GitHub tokens via gh auth token.

For each token found, it creates a new public repository named with random Slavic folklore words (e.g., BABA-YAGA-KOSCHEI-742, description: “PUSH UR T3MPRR”) and uploads the encrypted data bundle as results.json. The attacker can later search GitHub for repositories matching these distinctive naming patterns to retrieve the exfiltrated data.

1. No trusted publishers. The project uses legacy API token authentication instead of PyPI’s OIDC trusted publisher mechanism. Trusted publishers bind publishing to a specific GitHub repository, workflow, and environment. A stolen token cannot publish from outside that workflow. This project has no such binding: anyone holding the token can upload any version from any machine.

Kubernetes (collectors/kubernetes.py): Parses kubeconfig (with a custom YAML parser, no PyYAML dependency), iterates every context, and dumps secrets from all namespaces. Supports in-cluster service account tokens, client certificate auth, and bearer tokens. If kubectl is not present, the collector downloads it from dl.k8s.io. After collecting secrets, it propagates the payload to up to 5 other running pods via kubectl exec.

To measure the adversarial robustness of AI agents, we introduce AgentDojo, an evaluation framework for agents that execute tools over untrusted data. To capture the evolving nature of attacks and defenses, AgentDojo is not a static test suite, but rather an extensible environment for designing and evaluating new agent tasks, defenses, and adaptive attacks. We populate the environment with 97 realistic tasks (e.g., managing an email client, navigating an e-banking website, or making travel bookings), 629 security test cases, and various attack and defense paradigms from the literature. We find that AgentDojo poses a challenge for both attacks and defenses: state-of-the-art LLMs fail at many tasks (even in the absence of attacks), and existing prompt injection attacks break some security properties but not all. We hope that AgentDojo can foster research on new design principles for AI agents that solve common tasks in a reliable and robust manner.

ChatGPT can directly run Bash commands now. Previously it was limited to Python code only, although it could run shell commands via the Python subprocess module. It has Node.js and can run JavaScript directly in addition to Python. I also got it to run “hello world” in Ruby, Perl, PHP, Go, Java, Swift, Kotlin, C and C++. No Rust yet though! While the container still can’t make outbound network requests, pip install package and npm install package both work now via a custom proxy mechanism. ChatGPT can locate the URL for a file on the web and use a container.download tool to download that file and save it to a path within the sandboxed container.

Is this a data exfiltration vulnerability though? Could a prompt injection attack trick ChatGPT into leaking private data out to a container.download call to a URL with a query string that includes sensitive information?

I don’t think it can. I tried getting it to assemble a URL with a query string and access it using container.download and it couldn’t do it. It told me that it got back this error:

ERROR: download failed because url not viewed in conversation before. open the file or url using web.run first.

This looks to me like the same safety trick used by Claude’s Web Fetch tool: only allow URL access if that URL was either directly entered by the user or if it came from search results that could not have been influenced by a prompt injection.