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EUVD-2026-330766

fkie_cve-2026-44018

Docling simplifies document processing by parsing diverse formats and providing integrations with the generative AI ecosystem. From 2.45.0 until 2.91.0, the METS-GBS backend's XML parsing and the input document format detection lacked security controls. An attacker could craft malicious METS-GBS archives that, when processed, could read sensitive files, exhaust system resources, or cause application crashes. This vulnerability is fixed in 2.91.0.

GHSA-r3xg-rg9j-67fv — Docling: Unsafe Archive Extraction and XML Parsing in METS-GBS Backend

Affected: PyPI: docling

### Impact The METS-GBS backend's XML parsing and the input document format detection lacked security controls, enabling: - XML External Entity (XXE) attacks to read local files or cause denial of service - Decompression bombs (zip bombs) to exhaust memory and disk space - Unbounded archive extraction consuming system resources

An attacker could craft malicious METS-GBS archives that, when processed, could read sensitive files, exhaust system resources, or cause application crashes.

### Patches Fixed in version 2.91.0. The fix implements: - Secure XML parsing with `resolve_entities=False`, `load_dtd=False`, and `no_network=True` - Configurable limits: 300 MB total extraction size, 10 MB per file, 1000 member count - Cumulative size tracking across all extractions - Early termination when limits are exceeded - Secure format detection of METS-GBS tar archives with `_detect_mets_gbs()` method: maximum file size (10 MB per file), maximum member count (1000 members), and exception handling to gracefully fail when limits are exceeded

### Workarounds Avoid processing METS-GBS archives from untrusted sources. If necessary, pre-validate archives in an isolated environment with resource limits.

### References - Fix release: [v2.91.0](https://github.com/docling-project/docling/releases/tag/v2.91.0)

PYSEC-2026-2144

Affected: PyPI: docling

RHSA-2026:60520 — Red Hat Security Advisory: RHOAI 3.4.4 - Red Hat OpenShift AI

urllib3: urllib3 Streaming API improperly handles highly compressed data github.com/argoproj/argo-workflows: argoproj/argo-workflows is vulnerable to RCE via ZipSlip and symbolic links image-size: image-size: Denial of Service via crafted image buffer with zero-valued size field image-size: image-size: Denial of Service via crafted ICNS image buffer mlflow/mlflow: mlflow/mlflow: Unauthenticated remote code execution via unprotected job endpoints mlflow: mlflow: Arbitrary file read via bypassed source path validation python-transformers: python-transformers: Arbitrary code execution due to overridden trust_remote_code setting jupyter-server: jupyter-server: Sensitive data exposure via path traversal vulnerability fast-uri: fast-uri: URI authority bypass due to improper delimiter handling undici: undici: Information disclosure and data integrity issues due to incorrect Socks5ProxyAgent connection routing python-pip: Path traversal via malicious entry point name in pip wheel installation allows arbitrary file overwrite undici: undici: Man-in-the-Middle attack via ignored TLS options with SOCKS5 proxy form-data: form-data: Form field override via CRLF injection undici: undici: Denial of Service due to unbounded memory growth via WebSocket frames nltk: NLTK: Information disclosure via path traversal vulnerability keras: Keras: Arbitrary code execution via deserialization vulnerability brace-expansion: Brace-expansion: Denial of Service due to exponential-time complexity shell-quo…