langchain-ai/langchain: Server-Side Request Forgery
CVE-2024-3095 is a high-severity server-side request forgery vulnerability in langchain-ai/langchain from LangChain AI, with a CVSS 3.1 base score of 7.7 out of 10. It can be exploited remotely over the network by an attacker with a low-privileged account, without any action from a user. Affected langchain-ai/langchain versions: latest and earlier. The record does not name a fixed version, so check the vendor advisory. There is no confirmed exploitation so far. EPSS estimates a 0.69% chance of exploitation within 30 days, higher than 53% of scored CVEs.
High severity with no evidence of exploitation so far.
What to do
- Check which version of langchain-ai/langchain you run against the affected versions below, and install the vendor's latest release.
Affected products and versions
| Vendor | Product | Affected versions | Fixed in |
|---|---|---|---|
| Langchain-ai | langchain-ai/langchain | latest and earlier | Not stated |
| Langchain | langchain | all versions | Not stated |
Version data comes from the organisation that assigned the CVE. Confirm against the vendor advisory before deciding you are unaffected.
How the severity score breaks down
- Attack vector
- NetworkCan be exploited remotely over a network, including the internet.
- Attack complexity
- LowNo special conditions are needed; the attack works reliably.
- Privileges required
- LowA basic user account is enough.
- User interaction
- NoneNo action by a victim is required.
- Scope
- ChangedThe attack can reach beyond the vulnerable component into other systems.
- Confidentiality
- HighAll data handled by the component can be read.
- Integrity
- NoneNothing can be modified.
- Availability
- NoneAvailability is not affected.
Vector: CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:N/A:N. Scored by NVD.
| Scoring system | Score | Severity | Scored by |
|---|---|---|---|
| CVSS 3.1 | 7.7 | High | NVD |
| CVSS 3.0 | 4.8 | Medium | CNA (huntr.dev) |
Official description
A Server-Side Request Forgery (SSRF) vulnerability exists in the Web Research Retriever component of langchain-ai/langchain version 0.1.5. The vulnerability arises because the Web Research Retriever does not restrict requests to remote internet addresses, allowing it to reach local addresses. This flaw enables attackers to execute port scans, access local services, and in some scenarios, read instance metadata from cloud environments. The vulnerability is particularly concerning as it can be exploited to abuse the Web Explorer server as a proxy for web attacks on third parties and interact with servers in the local network, including reading their response data. This could potentially lead to arbitrary code execution, depending on the nature of the local services. The vulnerability is limited to GET requests, as POST requests are not possible, but the impact on confidentiality, integrity, and availability is significant due to the potential for stolen credentials and state-changing interactions with internal APIs.
Source: the CVE record as published by its numbering authority, via NVD and CVE.org.
The underlying weakness: Server-Side Request Forgery
The web server receives a URL or similar request from an upstream component and retrieves the contents of this URL, but it does not sufficiently ensure that the request is being sent to the expected destination.
How developers prevent this class of flaw
- If there is a known list of applications, IP, hostname, and/or domains to which the product is allowed to communicate, then use allowlists to ensure that outgoing requests are restricted to these known entities. The allowlists could be in the application code or possibly enforced by firewalls or network segmentation.
- If the product is expected to be able to connect to arbitrary IP addresses or domains, consider using a denylist that includes private network addresses, internal domain names, etc. The denylist could be in the application code or possibly enforced by firewalls or network segmentation.
Timeline
- CVE record published.
- CISA published its SSVC assessment (exploitation: poc, automatable: no, technical impact: partial).
- Record last changed materially (score, affected products, status or references).
References
Technical write-ups (may include proof of concept)
- huntr.com/bounties/e62d4895-2901-405b-9559-38276b6a5273Exploit, Third Party Advisory
Other vulnerabilities in langchain-ai/langchain
- 7.3 High severity
- 7.5 High severity
- 7.5 High severity
- 10.0 Critical severity
- 9.8 Critical severity
- 7.8 High severity
Recent Server-Side Request Forgery vulnerabilities
- 9.3 Critical severity
- 7.2 High severity
- 7.7 High severity
- 8.8 High severity
- 9.8 Critical severity
- 8.6 High severity
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