PatchSiren

PatchSiren cyber security CVE debrief

CVE-2026-1116 parisneo CVE debrief

A Cross-site Scripting (XSS) vulnerability was identified in the `from_dict` method of the `AppLollmsMessage` class in parisneo/lollms prior to version 2.2.0. The vulnerability arises from the lack of sanitization or HTML encoding of the `content` field when deserializing user-provided data. This allows an attacker to inject malicious HTML or JavaScript payloads, which can be executed in the context of another user's browser.

Vendor
parisneo
Product
parisneo/lollms
CVSS
MEDIUM 6.1
CISA KEV
Not listed in stored evidence
Original CVE published
2026-04-12
Original CVE updated
2026-10-26
Advisory published
2026-04-12
Advisory updated
2026-10-26

Who should care

Defenders responsible for lollms installations should assess exposure to this XSS vulnerability and prioritize patching or mitigation efforts. This includes operators, platform administrators, vulnerability management teams, and security teams who need to verify affected versions, perform inventory checks, and implement compensating controls until patches are applied.

Why it matters

Defenders should prioritize verifying exposure of lollms installations to this XSS vulnerability and apply patches or mitigations as available. This vulnerability allows attackers to inject malicious HTML or JavaScript payloads, potentially leading to account takeover, session hijacking, or wormable attacks. Verification of affected versions, inventory checks, and compensating controls are necessary until patches are applied.

  • Account takeover or session hijacking
  • Wormable attacks through injected malicious payloads
  • Execution of unauthorized scripts in user browsers
  • Potential for phishing or further exploitation

Technical summary

The vulnerability arises from the lack of sanitization or HTML encoding of the `content` field when deserializing user-provided data. This allows an attacker to inject malicious HTML or JavaScript payloads, which can be executed in the context of another user's browser. The vulnerability is identified in the `from_dict` method of the `AppLollmsMessage` class in parisneo/lollms prior to version 2.2.0. Exploitation of this vulnerability can lead to account takeover, session hijacking, or wormable attacks. Defenders should prioritize verifying exposure of lollms installations to this XSS vulnerability and apply patches or mitigations as available.

Defensive priority

Defenders should prioritize verifying exposure of lollms installations to this XSS vulnerability and apply patches or mitigations as available.

Recommended defensive actions

  • Verify lollms installations for exposure to this XSS vulnerability
  • Apply patches or mitigations as available
  • Monitor for suspicious activity related to this vulnerability
  • Review compensating controls for exposed systems while remediation is scheduled and verified
  • Check relevant monitoring, detection, and logs for exposed assets that need extra review
  • Track exceptions, retest remediated assets, and close the item only after evidence is documented
  • Confirm whether affected product deployments exist in managed environments and assign an owner for follow-up

Evidence notes

The CVE record and NVD entry provide details on the vulnerability, including its CVSS score of 6.1 and severity of MEDIUM. The vulnerability is described as a Cross-site Scripting (XSS) issue in the `from_dict` method of the `AppLollmsMessage` class in parisneo/lollms prior to version 2.2.0.

Sources and references

Verified primary and authoritative sources

  • CVE-2026-1116 CVE Program record

    Publisher, destination, and source semantics verified

    URL: https://www.cve.org/CVERecord?id=CVE-2026-1116

    CVE Program - Official CVE Program record with source-provided CVE metadata.

  • CVE-2026-1116 NVD vulnerability detail

    Publisher, destination, and source semantics verified

    URL: https://nvd.nist.gov/vuln/detail/CVE-2026-1116

    NIST National Vulnerability Database - Official NIST NVD detail page and source-specific vulnerability assessment.

Supplemental references

Methodology and review provenance

AI-assisted synthesis based on stored public vulnerability evidence. System validation, approval state, and publication status do not by themselves establish human review of this revision. PatchSiren helps prioritize defensive review and does not prove exposure or remediation on any system.