CVE-2024-48142VulnerabilityPublished 24 Oct 2024Updated 25 Oct 2024

CVE-2024-48142: n/a

Source CVE Database V5

Not known to be exploited · CVSS 7.5 High.

Data as of 26 Oct 2026, 07:57 UTC

CVSS 3.1 base score

7.5High

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

EPSS: 0.4% chance of exploitation in 30 days – higher than 35% 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:H/I:N/A:N

Attack Vector: Network
Attack Complexity: Low
Privileges Required: None
User Interaction: None
Scope: Unchanged
Confidentiality: High
Integrity: None
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: High: There is total loss of confidentiality, resulting in all resources within the impacted component being divulged to the attacker. Alternatively, access to only some restricted information is obtained, but the disclosed information presents a direct, serious impact.
Integrity: None: There is no loss of integrity within 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 24 Oct 2024
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 2026-10-26.

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

AI-suggested

1. Immediate mitigation should focus on restricting network access to the Monica ChatGPT AI Assistant chatbox interface, limiting it to trusted internal networks or VPNs to reduce exposure. 2. Implement input validation and sanitization at the application layer to detect and block malicious prompt injection patterns, specifically targeting command injection vectors (CWE-77). 3. Monitor and audit chat logs for unusual patterns or unexpected data access attempts that may indicate exploitation attempts. 4. Employ Web Application Firewalls (WAFs) with custom rules designed to detect and block injection payloads targeting the chatbox. 5. Coordinate with Butterfly Effect Limited for timely patch releases and apply updates as soon as they become available. 6. Educate users and administrators about the risks of prompt injection and encourage cautious handling of AI assistant interactions. 7. Consider deploying AI assistant instances in isolated environments with strict data access controls to minimize potential data leakage. 8. Conduct penetration testing and security assessments focused on AI assistant interfaces to identify and remediate similar vulnerabilities proactively.

Description

A prompt injection vulnerability in the chatbox of Butterfly Effect Limited Monica ChatGPT AI Assistant v2.4.0 allows attackers to access and exfiltrate all previous and subsequent chat data between the user and the AI assistant via a crafted message.

Impact

The primary impact of CVE-2024-48142 is the unauthorized disclosure of sensitive chat data, which can include confidential business information, personally identifiable information (PII), or proprietary communication. Organizations relying on Monica ChatGPT AI Assistant for customer support, internal collaboration, or decision-making processes could face data breaches leading to reputational damage, regulatory penalties (e.g., GDPR, HIPAA), and loss of customer trust. Since the vulnerability allows access to both historical and future chat data, attackers could continuously monitor ongoing communications, potentially facilitating further attacks such as social engineering or corporate espionage. The ease of exploitation without authentication or user interaction increases the risk of widespread abuse. Although availability and integrity are not directly impacted, the confidentiality breach alone is significant, especially in sectors handling sensitive data such as finance, healthcare, and government. The absence of known exploits in the wild currently limits immediate risk but does not preclude future exploitation once the vulnerability becomes widely known.

CWE-77Improper Neutralization of Special Elements used in a Command ('Command Injection') (opens in a new tab)
Class weaknessLikelihood of exploit: HighCWE Top 25 (2025): #23OWASP Top 10 A05:2025: Injection (opens in a new tab)
The product constructs all or part of a command using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the intended command when it is sent to a downstream component.
Architecture and Design
If at all possible, use library calls rather than external processes to recreate the desired functionality.
Implementation
If possible, ensure that all external commands called from the program are statically created.
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.
OWASP Cheat Sheets
Abuse Case (opens in a new tab)
Deserialization (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
V1.5.1 (opens in a new tab)
Attack patterns
LDAP Injection (opens in a new tab)
Command Delimiters (opens in a new tab)
IMAP/SMTP Command Injection (opens in a new tab)
Manipulating Writeable Terminal Devices (opens in a new tab)
Exploiting Multiple Input Interpretation Layers (opens in a new tab)
From MITRE CWE, MITRE CAPEC and OWASP (Top 10, OpenCRE).

References

Community reviews

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Exploitation evidence

What to do

Description

AI analysisMachine-generated, updated 26 Feb 2026, 08:52 UTC

WeaknessesCWE-77

Technical details

References

Community reviews

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