VulnerabilityPublished 16 Mar 2026Updated 17 Mar 2026

CVE-2026-4276: LibreChat RAG API · Improper Input Validation

Affects LibreChat RAG APISource CVE Database V5

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Not known to be exploited · CVSS 7.5 High.

Data as of 30 Oct 2026, 11:52 UTC

CVSS 3.1 base score

7.5High

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

EPSS: 0.2% chance of exploitation in 30 days – higher than 20% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.

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

Attack Vector: Network
Attack Complexity: Low
Privileges Required: None
User Interaction: None
Scope: Unchanged
Confidentiality: None
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: None: The attacker is unauthorized prior to attack, and therefore does not require any access to settings or files to carry out an attack.
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: None: There is no loss of confidentiality within 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 16 Mar 2026
Detectable: no evidence on record.
No public nuclei template indexed
Weaponized: 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 30 Oct 2026.

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.
Weaponized: A Metasploit exploit module or Exploit-DB entry exists (Metasploit scanner modules do not count), or CISA SSVC or the CVSS exploit maturity 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

Affected software

Product	Affected	Fixed in
LibreChatRAG API	Affected =0.7.0	Fixed in–

AI-suggested

Immediate mitigation involves restricting network access to the LibreChat RAG API 0.7.0 instance, limiting exposure to trusted internal networks only.
Implement input validation and sanitization at the application layer to filter or escape potentially malicious characters before logging.
Employ centralized logging solutions that can detect anomalies or suspicious log patterns indicative of injection attempts.
Monitor logs for unusual entries or patterns that could indicate tampering or injection.
Engage with the LibreChat development community or vendor to obtain or contribute patches that properly sanitize log inputs.
Consider deploying Web Application Firewalls (WAFs) or intrusion detection systems (IDS) with rules targeting log injection attack signatures.
Maintain strict access controls and audit trails on log management systems to detect unauthorized changes.
Plan for timely updates once an official patch is released, and test updates in staging environments before deployment.
Educate security teams about the risks of log injection and the importance of log integrity in incident response.

Description

CVE-2026-4276 is a high-severity vulnerability in LibreChat RAG API version 0.7.0 that allows attackers to perform log injection by exploiting improper input validation. This flaw enables adversaries to forge or manipulate log entries, potentially obscuring malicious activities or injecting misleading information into logs. The vulnerability requires no authentication or user interaction and can be exploited remotely over the network. While it does not directly impact confidentiality or availability, it severely compromises the integrity of log data, which is critical for incident detection and forensic analysis. No known exploits are currently reported in the wild, and no patches have been released yet. Organizations relying on LibreChat RAG API 0.7.0 should prioritize monitoring and mitigation to prevent attackers from leveraging this vulnerability to cover their tracks or mislead security teams.

Impact

The primary impact of this vulnerability is on the integrity of log data, which is essential for security monitoring, incident response, and forensic investigations. Attackers exploiting this flaw can inject misleading or false entries into logs, potentially hiding their malicious activities or creating confusion during incident analysis. This can delay detection of breaches, allow persistent attackers to remain undetected, and reduce the overall security posture of affected organizations. While confidentiality and availability are not directly compromised, the undermining of log integrity can indirectly facilitate more severe attacks by masking them. Organizations using LibreChat RAG API 0.7.0 in production environments, especially those relying on logs for compliance and security auditing, face increased risk of undetected intrusions and compliance violations. The remote and unauthenticated nature of the exploit increases the likelihood of exploitation if the vulnerable version is exposed to untrusted networks.

Summary

CVE-2026-4276 identifies a log-injection vulnerability in LibreChat RAG API version 0.7.0, stemming from improper input validation (CWE-20) and improper neutralization of log data (CWE-117). The vulnerability allows attackers to inject crafted input that is logged by the system without proper sanitization, enabling them to forge or manipulate log entries. This can mislead administrators and security analysts by inserting false log data or hiding malicious activities, thereby undermining trust in audit trails. The vulnerability is remotely exploitable without authentication or user interaction, increasing its risk profile. The CVSS 3.1 base score is 7.5 (high), reflecting the network attack vector, low attack complexity, no privileges required, and no user interaction needed. The impact is primarily on integrity, as confidentiality and availability are not affected. No patches or known exploits are currently available, indicating the need for proactive mitigation. The vulnerability affects only version 0.7.0 of LibreChat RAG API, an open-source AI chat retrieval-augmented generation API, which is used in AI-driven conversational systems. The lack of input validation and log sanitization highlights a critical security oversight in handling user-supplied data within logging mechanisms.

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).
CWE-117Improper Output Neutralization for Logs (opens in a new tab)
Base weaknessLikelihood of exploit: MediumOWASP Top 10 A09:2025: Security Logging & Alerting Failures (opens in a new tab)
The product constructs a log message from external input, but it does not neutralize or incorrectly neutralizes special elements when the message is written to a log file.
Implementation · Input Validation
Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.
Implementation · Output Encoding
Use and specify an output encoding that can be handled by the downstream component that is reading the output. Common encodings include ISO-8859-1, UTF-7, and UTF-8.
Implementation · Input Validation
Inputs should be decoded and canonicalized to the application's current internal representation before being validated (CWE-180). Make sure that the application does not decode the same input twice (CWE-174).
OWASP Cheat Sheets
Logging (opens in a new tab)
Content Security Policy (opens in a new tab)
Application Logging Vocabulary (opens in a new tab)
OWASP ASVS 4.0.3
V7.3.1 (opens in a new tab)
V14.4.6 (opens in a new tab)
V14.4.4 (opens in a new tab)
Attack patterns
Audit Log Manipulation (opens in a new tab)
Web Server Logs Tampering (opens in a new tab)
Log Injection-Tampering-Forging (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 24 Mar 2026, 00:59 UTC

WeaknessesCWE-20, CWE-117

Technical details

References

Community reviews

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