CVE-2025-62496 (GCVE-0-2025-62496)
Vulnerability from cvelistv5 – Published: 2025-10-16 15:52 – Updated: 2025-10-16 17:26
VLAI
EPSS
VEX
Title
Integer overflow in js_bigint_from_string in QuickJS
Summary
A vulnerability exists in the QuickJS engine's BigInt string parsing logic (js_bigint_from_string) when attempting to create a BigInt from a string with an excessively large number of digits.
The function calculates the necessary number of bits (n_bits) required to store the BigInt using the formula:
$$\text{n\_bits} = (\text{n\_digits} \times 27 + 7) / 8 \quad (\text{for radix 10})$$
* For large input strings (e.g., $79,536,432$ digits or more for base 10), the intermediate calculation $(\text{n\_digits} \times 27 + 7)$ exceeds the maximum value of a standard signed 32-bit integer, resulting in an Integer Overflow.
* The resulting n_bits value becomes unexpectedly small or even negative due to this wrap-around.
* This flawed n_bits is then used to compute n_limbs, the number of memory "limbs" needed for the BigInt object. Since n_bits is too small, the calculated n_limbs is also significantly underestimated.
* The function proceeds to allocate a JSBigInt object using this underestimated n_limbs.
* When the function later attempts to write the actual BigInt data into the allocated object, the small buffer size is quickly exceeded, leading to a Heap Out-of-Bounds Write as data is written past the end of the allocated r->tab array.
Severity
SSVC
Exploitation: poc
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-10-16 17:26 UTC
CWE
- CWE-190 - Integer Overflow or Wraparound
Assigner
References
2 references
Impacted products
Date Public
2025-07-24 22:00
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"cveId": "CVE-2025-62496",
"datePublished": "2025-10-16T15:52:05.654Z",
"dateReserved": "2025-10-15T08:47:41.878Z",
"dateUpdated": "2025-10-16T17:26:25.654Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.1"
}
}
}
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Drag nodes to arrange them. Double-click a related vulnerability to open it, or a source to visit its link. The Physics button on the left tunes the layout and the simulation; the legend at the bottom left hides or shows nodes by kind and by sighting type.
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.
Vulnerability timeline
Sightings and recorded dates, grouped by day · UTC
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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.