CVE-2026-25126VulnerabilityPublished 29 Jan 2026Updated 2 Feb 2026

CVE-2026-25126: polarnl PolarLearn · Improper Input Validation

Affects polarnl PolarLearnSource CVE Database V5

Not known to be exploited · CVSS 7.1 High : upgrade to 0-PRERELEASE-15 soon.

Data as of 25 Oct 2026, 19:02 UTC

CVSS 3.1 base score

7.1High

How severe the flaw is on the 0–10 CVSS scale.

EPSS: 0.3% chance of exploitation in 30 days – higher than 31% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch: Available – fixed in 0-PRERELEASE-15

CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:H/A:N

Attack Vector: Network
Attack Complexity: Low
Privileges Required: Low
User Interaction: None
Scope: Unchanged
Confidentiality: Low
Integrity: High
Availability: None

Bold values are the worst case: easiest to exploit or largest impact.

Attack Vector: Network: The vulnerable component is bound to the network stack and the set of possible attackers extends beyond the other options listed, up to and including the entire Internet. Such a vulnerability is often termed 'remotely exploitable' and can be thought of as an attack being exploitable at the protocol level one or more network hops away (e.g., across one or more routers).
Attack Complexity: Low: Specialized access conditions or extenuating circumstances do not exist. An attacker can expect repeatable success against the vulnerable component.
Privileges Required: Low: The attacker is authorized with (i.e., requires) privileges that provide basic user capabilities that could normally affect only settings and files owned by a user. Alternatively, an attacker with Low privileges may have the ability to cause an impact only to non-sensitive resources.
User Interaction: None: The vulnerable system can be exploited without any interaction from any user.
Scope: Unchanged: An exploited vulnerability can only affect resources managed by the same security authority. In this case, the vulnerable component and the impacted component are either the same, or both are managed by the same security authority.
Confidentiality: Low: There is some loss of confidentiality. Access to some restricted information is obtained, but the attacker does not have control over what information is obtained, or the amount or kind of loss is limited. The information disclosure does not cause a direct, serious loss to the impacted component.
Integrity: High: There is a total loss of integrity, or a complete loss of protection. For example, the attacker is able to modify any/all files protected by the impacted component. Alternatively, only some files can be modified, but malicious modification would present a direct, serious consequence to the impacted component.
Availability: None: There is no impact to availability within the impacted component.

Definitions: FIRST CVSS v3.1 specification.

Exploitation evidence

Disclosed: evidence found.
CVE published 29 Jan 2026
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by Mirage: no evidence on record.
No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 2026-10-25.

Disclosed: The CVE record is public on cve.org. Its publication is t = 0.
Detectable: A public nuclei template can find exposed instances, for defenders and attackers alike.
Weaponised: Public exploit code exists in Metasploit or Exploit-DB, or CISA SSVC reports a public proof of concept.
Exploited in the wild: CISA KEV lists it, CISA SSVC rates exploitation active, or a source reports attacks.
Observed by OffSeq Mirage: OffSeq Mirage honeypot sensors recorded attack traffic for this CVE, or found exposed hosts without attack traffic.

Moments are spaced evenly, not to scale; a dashed line marks a gap of six months or more. Deltas count from t = 0. Sources that give only a date (CISA KEV, Exploit-DB, or a time of exactly 00:00Z) read “same day” rather than hours. Metasploit modules carry the flaw’s disclosure date, not their release date, and nuclei templates carry no date, so both appear above but not on the timeline.

What to do

Upgrade polarnl PolarLearn to 0-PRERELEASE-15.

Affected software

Product	Affected	Fixed in
polarnlPolarLearn	Affected <0-PRERELEASE-15	Fixed in 0-PRERELEASE-15
GitHubpolarnl/PolarLearn`pkg:github/polarnl/PolarLearn`Other threats	Affected–	Fixed in–

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

European organizations should immediately upgrade PolarLearn installations to version 0-PRERELEASE-15 or later to ensure proper input validation is enforced. In addition to patching, implement the following measures: 1) Conduct a thorough audit of vote-related data to detect and remediate any anomalies or suspicious entries caused by this vulnerability. 2) Employ Web Application Firewalls (WAFs) with custom rules to detect and block malformed or unexpected 'direction' values in API requests until patches are deployed. 3) Restrict network access to the vote API endpoint to trusted users and systems, using network segmentation and access controls to minimize exposure. 4) Implement runtime input validation at the API gateway or proxy level as an additional safeguard against malformed inputs. 5) Monitor logs for unusual voting patterns or repeated invalid input attempts to identify potential exploitation attempts early. 6) Educate developers and administrators about the limitations of relying solely on TypeScript types for input validation and promote secure coding practices that include runtime checks. 7) Review and strengthen business logic validation downstream to reject invalid or unexpected data before persistence. These targeted actions will reduce the risk of exploitation and help maintain data integrity.

Description

PolarLearn is a free and open-source learning program. Prior to version 0-PRERELEASE-15, the vote API route (`POST /api/v1/forum/vote`) trusts the JSON body’s `direction` value without runtime validation. TypeScript types are not enforced at runtime, so an attacker can send arbitrary strings (e.g., `"x"`) as `direction`. Downstream (`VoteServer`) treats any non-`"up"` and non-`null` value as a downvote and persists the invalid value in `votes_data`. This can be exploited to bypass intended business logic. Version 0-PRERELEASE-15 fixes the vulnerability.

Impact

For European organizations, especially those in education or training sectors using PolarLearn, this vulnerability poses a risk to the integrity of user-generated content and feedback mechanisms. Manipulated vote data can undermine trust in forum discussions, assessments, or peer reviews, potentially affecting learning outcomes and decision-making processes. Since the vulnerability allows low-privilege attackers to bypass business logic, it could be exploited to skew reputations, promote misinformation, or disrupt collaborative learning environments. Although it does not compromise confidentiality or availability, the integrity breach can lead to reputational damage and loss of confidence in the platform. Additionally, organizations relying on vote data for analytics or automated actions may experience erroneous results, impacting operational decisions. The lack of user interaction requirement and network-level exploitability increase the threat surface, especially in environments with exposed APIs or insufficient network segmentation. European institutions with strict data integrity and compliance requirements may face regulatory scrutiny if manipulated data affects educational records or assessments.

CWE-20Improper Input Validation (opens in a new tab)
Class weaknessLikelihood of exploit: HighCWE Top 25 (2025): #18OWASP Top 10 A05:2025: Injection (opens in a new tab)
The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly.
MITRE discourages mapping vulnerabilities to this broad entry; the root cause is usually a more specific weakness.
Architecture and Design · Attack Surface Reduction
Consider using language-theoretic security (LangSec) techniques that characterize inputs using a formal language and build "recognizers" for that language. This effectively requires parsing to be a distinct layer that effectively enforces a boundary between raw input and internal data representations, instead of…
Architecture and Design · Libraries or Frameworks
Use an input validation framework such as Struts or the OWASP ESAPI Validation API. Note that using a framework does not automatically address all input validation problems; be mindful of weaknesses that could arise from misusing the framework itself (CWE-1173).
Architecture and Design · Implementation · Attack Surface Reduction
Understand all the potential areas where untrusted inputs can enter the product, including but not limited to: parameters or arguments, cookies, anything read from the network, environment variables, reverse DNS lookups, query results, request headers, URL components, e-mail, files, filenames, databases, and any…
OWASP Cheat Sheets
XML Security (opens in a new tab)
Mass Assignment (opens in a new tab)
Injection Prevention (opens in a new tab)
SQL Injection Prevention (opens in a new tab)
Injection Prevention in Java (opens in a new tab)
OWASP ASVS 4.0.3
V13.3.1 (opens in a new tab)
V5.1.4 (opens in a new tab)
V5.1.3 (opens in a new tab)
Attack patterns
Buffer Overflow via Environment Variables (opens in a new tab)
Server Side Include (SSI) Injection (opens in a new tab)
Cross Zone Scripting (opens in a new tab)
Command Line Execution through SQL Injection (opens in a new tab)
Object Relational Mapping Injection (opens in a new tab)
From MITRE CWE, MITRE CAPEC and OWASP (Top 10, OpenCRE).

References

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Exploitation evidence

What to do

Affected software

Description

AI analysisMachine-generated, updated 6 Feb 2026, 08:43 UTC

WeaknessesCWE-20

Technical details

References

Community reviews

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