CVE-2025-40916VulnerabilityPublished 16 Jun 2025

CVE-2025-40916: GRYPHON Mojolicious::Plugin::CaptchaPNG · Use of Cryptographically Weak Pseudo-Random Number Generator

Affects GRYPHON Mojolicious::Plugin::CaptchaPNGSource CVE Database V5

Not known to be exploited · CVSS 9.1 Critical.

Data as of 26 Oct 2026, 06:26 UTC

CVSS 3.1 base score

9.1Critical

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

EPSS: 0.4% chance of exploitation in 30 days – higher than 36% 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:H/A:N

Attack Vector: Network
Attack Complexity: Low
Privileges Required: None
User Interaction: None
Scope: Unchanged
Confidentiality: High
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: 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: 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 Jun 2025
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.

cwe-804

What to do

Affected software

Product	Affected	Fixed in
GRYPHON Mojolicious::Plugin::CaptchaPNG	Affected =1.05	Fixed in–

AI-suggested

Immediate mitigation should focus on replacing the insecure rand() function with a cryptographically secure pseudo-random number generator (CSPRNG). In Perl, this can be achieved by using modules such as Crypt::PRNG or leveraging system sources of entropy like /dev/urandom or the Crypt::Random module. Developers should update the CAPTCHA generation logic to use these secure sources for both the CAPTCHA text and image noise. Until an official patch is released, organizations should consider disabling the Mojolicious::Plugin::CaptchaPNG 1.05 or replacing it with alternative CAPTCHA solutions that use secure randomness. Additionally, implementing multi-layered bot mitigation strategies such as rate limiting, IP reputation checks, and behavioral analysis can reduce the risk of automated abuse. Monitoring web application logs for unusual patterns of CAPTCHA failures or repeated automated requests can help detect exploitation attempts. Organizations should also review their dependency management processes to quickly identify and update vulnerable components. Finally, educating developers about the importance of cryptographically secure randomness in security controls is essential to prevent similar issues.

Description

Mojolicious::Plugin::CaptchaPNG version 1.05 for Perl uses a weak random number source for generating the captcha. That version uses the built-in rand() function for generating the captcha text as well as image noise, which is insecure.

Impact

For European organizations, the impact of this vulnerability can be significant, especially for those relying on Mojolicious::Plugin::CaptchaPNG 1.05 to protect web forms, login pages, or other user interaction points from automated abuse. Successful exploitation could allow attackers to bypass CAPTCHA challenges, increasing the risk of automated credential stuffing, spam, fraudulent registrations, and denial of service through automated requests. This can lead to unauthorized access, data leakage, service disruption, and reputational damage. Organizations in sectors with high web interaction such as e-commerce, financial services, government portals, and online service providers are particularly at risk. The compromised CAPTCHA mechanism reduces the effectiveness of bot mitigation strategies, potentially increasing the attack surface. Additionally, since the weakness affects the integrity of the CAPTCHA challenge, it undermines trust in the affected applications’ security controls. However, the vulnerability does not directly compromise confidentiality or availability unless combined with other attack vectors. The absence of known exploits suggests that exploitation may require some technical effort or targeted knowledge, but the predictable nature of the weak PRNG makes automated attacks feasible once the weakness is understood.

CWE-338Use of Cryptographically Weak Pseudo-Random Number Generator (PRNG) (opens in a new tab)
Base weaknessLikelihood of exploit: MediumOWASP Top 10 A04:2025: Cryptographic Failures (opens in a new tab)
The product uses a Pseudo-Random Number Generator (PRNG) in a security context, but the PRNG's algorithm is not cryptographically strong.
Implementation
Use functions or hardware which use a hardware-based random number generation for all crypto. This is the recommended solution. Use CyptGenRandom on Windows, or hw_rand() on Linux.
OWASP Cheat Sheets
Transport Layer Protection (opens in a new tab)
User Privacy Protection (opens in a new tab)
Password Storage (opens in a new tab)
Cryptographic Storage (opens in a new tab)
HSTS (opens in a new tab)
OWASP ASVS 4.0.3
V6.3.3 (opens in a new tab)
V6.3.2 (opens in a new tab)
V6.3.1 (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

Affected software

Description

AI analysisMachine-generated, updated 16 Jun 2025, 11:20 UTC

WeaknessesCWE-338

Technical details

References

Community reviews

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