CVE-2026-34070
highPriorität · verfolgen · 10EPSS 0.014 · 72. PerzentilExploit · PoCDEEUUSEPSS · bewertet 2026-10-27
LangChain is a framework for building agents and LLM-powered applications. Prior to version 1.2.22, multiple functions in langchain_core.prompts.loading read files from paths embedded in deserialized config dicts without validating against directory traversal or absolute path injection. When an application passes user-influenced prompt configurations to load_prompt() or load_prompt_from_config(), an attacker can read arbitrary files on the host filesystem, constrained only by file-extension checks (.txt for templates, .json/.yaml for examples). This issue has been patched in version 1.2.22.
cvss
Technischer Schweregrad, 0–10. Eine Zeile je Quelle.
Basiswert 0–10 · höher = schwerwiegender · Vektor überfahren zum Auflösen
SSVC
Eine Handlungsentscheidung — aus Ausnutzung und Auswirkung.
schwächen
Die CWE-Schwächeklassen, zu denen dies gehört.
Exploits (1)
Öffentlicher Proof-of-Concept- und Exploit-Code.
betroffene Produkte (8)
Betroffene Produkte und Versionsbereiche.
- Red Hat Ansible Automation Platform 2.5alle Versionencert_bundDEcpe:/a:redhat:ansible_automation_platform:2.5
Advisories (3)
Hersteller- und Ökosystem-Meldungen dazu.
- WID-SEC-W-2026-0896LangChain: Schwachstelle ermöglicht Offenlegung von InformationenDeutsch2026-03-26csaf · cert_bundDE
Ein entfernter, anonymer Angreifer kann eine Schwachstelle in LangChain ausnutzen, um Informationen offenzulegen.
- GHSA-qh6h-p6c9-ff54LangChain Core has Path Traversal vulnerabilites in legacy `load_prompt` functions2026-03-27ghsa · osvUS
## Summary Multiple functions in `langchain_core.prompts.loading` read files from paths embedded in deserialized config dicts without validating against directory traversal or absolute path injection. When an application passes user-influenced prompt configurations to `load_prompt()` or `load_prompt_from_config()`, an attacker can read arbitrary files on the host filesystem, constrained only by file-extension checks (`.txt` for templates, `.json`/`.yaml` for examples). **Note:** The affected functions (`load_prompt`, `load_prompt_from_config`, and the `.save()` method on prompt classes) are undocumented legacy APIs. They are superseded by the `dumpd`/`dumps`/`load`/`loads` serialization APIs in `langchain_core.load`, which do not perform filesystem reads and use an allowlist-based security model. As part of this fix, the legacy APIs have been formally deprecated and will be removed in 2.0.0. ## Affected component **Package:** `langchain-core` **File:** `langchain_core/prompts/loading.py` **Affected functions:** `_load_template()`, `_load_examples()`, `_load_few_shot_prompt()` ## Severity **High** The score reflects the file-extension constraints that limit which files can be read. ## Vulnerable code paths | Config key | Loaded by | Readable extensions | |---|---|---| | `template_path`, `suffix_path`, `prefix_path` | `_load_template()` | `.txt` | | `examples` (when string) | `_load_examples()` | `.json`, `.yaml`, `.yml` | | `example_prompt_path` | `_load_few_shot_prompt()` | `.json`, `.yaml`, `.yml` | None of these code paths validated the supplied path against absolute path injection or `..` traversal sequences before reading from disk. ## Impact An attacker who controls or influences the prompt configuration dict can read files outside the intended directory: - **`.txt` files:** cloud-mounted secrets (`/mnt/secrets/api_key.txt`), `requirements.txt`, internal system prompts - **`.json`/`.yaml` files:** cloud credentials (`~/.docker/config.json`, `~/.azure/accessTokens.json`), Kubernetes manifests, CI/CD configs, application settings This is exploitable in applications that accept prompt configs from untrusted sources, including low-code AI builders and API wrappers that expose `load_prompt_from_config()`. ## Proof of concept ```python from langchain_core.prompts.loading import load_prompt_from_config # Reads /tmp/secret.txt via absolute path injection config = { "_type": "prompt", "template_path": "/tmp/secret.txt", "input_variables": [], } prompt = load_prompt_from_config(config) print(prompt.template) # file contents disclosed # Reads ../../etc/secret.txt via directory traversal config = { "_type": "prompt", "template_path": "../../etc/secret.txt", "input_variables": [], } prompt = load_prompt_from_config(config) # Reads arbitrary .json via few-shot examples config = { "_type": "few_shot", "examples": "../../../../.docker/config.json", "example_prompt": { "_type": "prompt", "input_variables": ["input", "output"], "template": "{input}: {output}", }, "prefix": "", "suffix": "{query}", "input_variables": ["query"], } prompt = load_prompt_from_config(config) ``` ## Mitigation **Update `langchain-core` to >= 1.2.22.** The fix adds path validation that rejects absolute paths and `..` traversal sequences by default. An `allow_dangerous_paths=True` keyword argument is available on `load_prompt()` and `load_prompt_from_config()` for trusted inputs. As described above, these legacy APIs have been formally deprecated. Users should migrate to `dumpd`/`dumps`/`load`/`loads` from `langchain_core.load`. ## Credit - [jiayuqi7813](https://github.com/jiayuqi7813) reporter - [VladimirEliTokarev](https://github.com/VladimirEliTokarev) reporter - [Rickidevs](https://github.com/Rickidevs) reporter - Kenneth Cox (cczine@gmail.com) reporter
- PYSEC-2026-21932026-03-31vendor · osvUS
LangChain is a framework for building agents and LLM-powered applications. Prior to version 1.2.22, multiple functions in langchain_core.prompts.loading read files from paths embedded in deserialized config dicts without validating against directory traversal or absolute path injection. When an application passes user-influenced prompt configurations to load_prompt() or load_prompt_from_config(), an attacker can read arbitrary files on the host filesystem, constrained only by file-extension checks (.txt for templates, .json/.yaml for examples). This issue has been patched in version 1.2.22.
auch bekannt als (6)
Weitere Namen und Kennungen dieser Entität.
quellen
Welche Quellen diesen Datensatz belegen und wann.
Referenzen (16)
Externe Belege — Patches, Meldungen, Analysen.
- https://github.com/langchain-ai/langchain/commit/27add913474e01e33bededf4096151130ba0d47ccnnvd
- https://github.com/langchain-ai/langchain/releasesPatchcnnvd
- https://github.com/langchain-ai/langchain/releases/tag/langchain-core==1.2.22cnnvd
- https://github.com/langchain-ai/langchain/security/advisories/GHSA-qh6h-p6c9-ff54cnnvd
- https://github.com/langchain-ai/langchain/commit/27add913474e01e33bededf4096151130ba0d47cenisa_euvd
- https://github.com/langchain-ai/langchain/releases/tag/langchain-core==1.2.22enisa_euvd
- https://github.com/langchain-ai/langchain/security/advisories/GHSA-qh6h-p6c9-ff54enisa_euvd
- https://access.redhat.com/errata/RHSA-2026:24766nvd
Angreifer nutzen Schwachstellen wie diese gegen im Internet exponierte Dienste aus. Sehen Sie mit einem kostenlosen Passiv-Scan, was exponiert ist.
Kostenloser Scan