CVE-2025-24369 (GCVE-0-2025-24369)
Vulnerability from cvelistv5 – Published: 2025-01-27 22:57 – Updated: 2025-01-28 15:18
VLAI
EPSS
VEX
Title
Anubis has a bot protection bypass when a sophisticated attacker asks to pass a challenge of difficulty 0
Summary
Anubis is a tool that allows administrators to protect bots against AI scrapers through bot-checking heuristics and a proof-of-work challenge to discourage scraping from multiple IP addresses. Anubis allows attackers to bypass the bot protection by requesting a challenge, formulates any nonce (such as 42069), and then passes the challenge with difficulty zero. Commit e09d0226a628f04b1d80fd83bee777894a45cd02 fixes this behavior by not using a client-specified difficulty value.
Severity
SSVC
Exploitation: none
Automatable: yes
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-01-28 14:59 UTC
CWE
- CWE-807 - Reliance on Untrusted Inputs in a Security Decision
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/Xe/x/security/advisories/GHSA-… | x_refsource_CONFIRM |
| https://github.com/Xe/x/commit/7bd7b209f4f1b897de… | x_refsource_MISC |
| https://github.com/Xe/x/commit/e09d0226a628f04b1d… | x_refsource_MISC |
| https://xeiaso.net/notes/2025/GHSA-56w8-8ppj-2p4f | x_refsource_MISC |
Impacted products
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"id": "CVE-2025-24369",
"lastModified": "2026-06-17T08:58:55.043",
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"modifiedAttackComplexity": "NOT_DEFINED",
"modifiedAttackRequirements": "NOT_DEFINED",
"modifiedAttackVector": "NOT_DEFINED",
"modifiedPrivilegesRequired": "NOT_DEFINED",
"modifiedSubAvailabilityImpact": "NOT_DEFINED",
"modifiedSubConfidentialityImpact": "NOT_DEFINED",
"modifiedSubIntegrityImpact": "NOT_DEFINED",
"modifiedUserInteraction": "NOT_DEFINED",
"modifiedVulnAvailabilityImpact": "NOT_DEFINED",
"modifiedVulnConfidentialityImpact": "NOT_DEFINED",
"modifiedVulnIntegrityImpact": "NOT_DEFINED",
"privilegesRequired": "LOW",
"providerUrgency": "NOT_DEFINED",
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"subConfidentialityImpact": "NONE",
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"userInteraction": "NONE",
"valueDensity": "NOT_DEFINED",
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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