CVE-2024-1544 (GCVE-0-2024-1544)
Vulnerability from cvelistv5 – Published: 2024-08-27 18:44 – Updated: 2026-01-27 21:58
VLAI
EPSS
VEX
Title
ECDSA nonce bias caused by truncation
Summary
Generating the ECDSA nonce k samples a random number r and then
truncates this randomness with a modular reduction mod n where n is the
order of the elliptic curve. Meaning k = r mod n. The division used
during the reduction estimates a factor q_e by dividing the upper two
digits (a digit having e.g. a size of 8 byte) of r by the upper digit of
n and then decrements q_e in a loop until it has the correct size.
Observing the number of times q_e is decremented through a control-flow
revealing side-channel reveals a bias in the most significant bits of
k. Depending on the curve this is either a negligible bias or a
significant bias large enough to reconstruct k with lattice reduction
methods. For SECP160R1, e.g., we find a bias of 15 bits.
Severity
4.1 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-08-27 19:14 UTC
CWE
- CWE-203 - Observable Discrepancy
Assigner
wolfSSL
CNA
References
Date Public
2024-07-08 17:30
{
"containers": {
"adp": [
{
"metrics": [
{
"other": {
"content": {
"id": "CVE-2024-1544",
"options": [
{
"Exploitation": "none"
},
{
"Automatable": "no"
},
{
"Technical Impact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2024-08-27T19:14:00.889289Z",
"version": "2.0.3"
},
"type": "ssvc"
}
}
],
"providerMetadata": {
"dateUpdated": "2024-08-27T19:14:35.190Z",
"orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"shortName": "CISA-ADP"
},
"title": "CISA ADP Vulnrichment"
}
],
"cna": {
"affected": [
{
"defaultStatus": "unknown",
"product": "wolfSSL",
"programFiles": [
"wolfcrypt/src/ecc.c"
],
"vendor": "wolfSSL",
"versions": [
{
"lessThanOrEqual": "5.6.4",
"status": "affected",
"version": "0",
"versionType": "Release"
}
]
}
],
"credits": [
{
"lang": "en",
"type": "finder",
"user": "00000000-0000-4000-9000-000000000000",
"value": "Florian Sieck, Luca Wilke and Thomas Eisenbarth from Universit\u00e4t zu L\u00fcbeck"
}
],
"datePublic": "2024-07-08T17:30:00.000Z",
"descriptions": [
{
"lang": "en",
"supportingMedia": [
{
"base64": false,
"type": "text/html",
"value": "Generating the ECDSA nonce k samples a random number r and then \u003cbr\u003etruncates this randomness with a modular reduction mod n where n is the \u003cbr\u003eorder of the elliptic curve. Meaning k = r mod n. The division used \u003cbr\u003eduring the reduction estimates a factor q_e by dividing the upper two \u003cbr\u003edigits (a digit having e.g. a size of 8 byte) of r by the upper digit of \u003cbr\u003en and then decrements q_e in a loop until it has the correct size. \u003cbr\u003eObserving the number of times q_e is decremented through a control-flow \u003cbr\u003erevealing side-channel reveals a bias in the most significant bits of \u003cbr\u003ek. Depending on the curve this is either a negligible bias or a \u003cbr\u003esignificant bias large enough to reconstruct k with lattice reduction \u003cbr\u003emethods. For SECP160R1, e.g., we find a bias of 15 bits."
}
],
"value": "Generating the ECDSA nonce k samples a random number r and then \ntruncates this randomness with a modular reduction mod n where n is the \norder of the elliptic curve. Meaning k = r mod n. The division used \nduring the reduction estimates a factor q_e by dividing the upper two \ndigits (a digit having e.g. a size of 8 byte) of r by the upper digit of \nn and then decrements q_e in a loop until it has the correct size. \nObserving the number of times q_e is decremented through a control-flow \nrevealing side-channel reveals a bias in the most significant bits of \nk. Depending on the curve this is either a negligible bias or a \nsignificant bias large enough to reconstruct k with lattice reduction \nmethods. For SECP160R1, e.g., we find a bias of 15 bits."
}
],
"impacts": [
{
"descriptions": [
{
"lang": "en",
"value": "Information Disclosure"
}
]
}
],
"metrics": [
{
"cvssV3_1": {
"attackComplexity": "HIGH",
"attackVector": "LOCAL",
"availabilityImpact": "NONE",
"baseScore": 4.1,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "HIGH",
"integrityImpact": "NONE",
"privilegesRequired": "HIGH",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:L/AC:H/PR:H/UI:N/S:U/C:H/I:N/A:N",
"version": "3.1"
},
"format": "CVSS",
"scenarios": [
{
"lang": "en",
"value": "GENERAL"
}
]
}
],
"problemTypes": [
{
"descriptions": [
{
"cweId": "CWE-203",
"description": "CWE-203 Observable Discrepancy",
"lang": "en",
"type": "CWE"
}
]
}
],
"providerMetadata": {
"dateUpdated": "2026-01-27T21:58:42.146Z",
"orgId": "50d2cd11-d01a-48ed-9441-5bfce9d63b27",
"shortName": "wolfSSL"
},
"references": [
{
"url": "https://github.com/wolfSSL/wolfssl/releases/tag/v5.7.2-stable"
},
{
"url": "https://github.com/wolfSSL/wolfssl/pull/7020"
}
],
"source": {
"discovery": "EXTERNAL"
},
"title": "ECDSA nonce bias caused by truncation",
"x_generator": {
"engine": "Vulnogram 0.1.0-dev"
}
}
},
"cveMetadata": {
"assignerOrgId": "50d2cd11-d01a-48ed-9441-5bfce9d63b27",
"assignerShortName": "wolfSSL",
"cveId": "CVE-2024-1544",
"datePublished": "2024-08-27T18:44:52.770Z",
"dateReserved": "2024-02-15T17:38:16.603Z",
"dateUpdated": "2026-01-27T21:58:42.146Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.2",
"vulnerability-lookup:meta": {
"vulnrichment": {
"containers": {
"adp": [
{
"metrics": [
{
"other": {
"content": {
"id": "CVE-2024-1544",
"options": [
{
"Exploitation": "none"
},
{
"Automatable": "no"
},
{
"Technical Impact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2024-08-27T19:14:00.889289Z",
"version": "2.0.3"
},
"type": "ssvc"
}
}
],
"providerMetadata": {
"dateUpdated": "2024-08-27T19:14:28.355Z",
"orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"shortName": "CISA-ADP"
},
"title": "CISA ADP Vulnrichment"
}
],
"cna": {
"affected": [
{
"defaultStatus": "unknown",
"product": "wolfSSL",
"programFiles": [
"wolfcrypt/src/ecc.c"
],
"vendor": "wolfSSL",
"versions": [
{
"lessThanOrEqual": "5.6.4",
"status": "affected",
"version": "0",
"versionType": "Release"
}
]
}
],
"credits": [
{
"lang": "en",
"type": "finder",
"user": "00000000-0000-4000-9000-000000000000",
"value": "Florian Sieck, Luca Wilke and Thomas Eisenbarth from Universit\u00e4t zu L\u00fcbeck"
}
],
"datePublic": "2024-07-08T17:30:00.000Z",
"descriptions": [
{
"lang": "en",
"supportingMedia": [
{
"base64": false,
"type": "text/html",
"value": "Generating the ECDSA nonce k samples a random number r and then \u003cbr\u003etruncates this randomness with a modular reduction mod n where n is the \u003cbr\u003eorder of the elliptic curve. Meaning k = r mod n. The division used \u003cbr\u003eduring the reduction estimates a factor q_e by dividing the upper two \u003cbr\u003edigits (a digit having e.g. a size of 8 byte) of r by the upper digit of \u003cbr\u003en and then decrements q_e in a loop until it has the correct size. \u003cbr\u003eObserving the number of times q_e is decremented through a control-flow \u003cbr\u003erevealing side-channel reveals a bias in the most significant bits of \u003cbr\u003ek. Depending on the curve this is either a negligible bias or a \u003cbr\u003esignificant bias large enough to reconstruct k with lattice reduction \u003cbr\u003emethods. For SECP160R1, e.g., we find a bias of 15 bits."
}
],
"value": "Generating the ECDSA nonce k samples a random number r and then \ntruncates this randomness with a modular reduction mod n where n is the \norder of the elliptic curve. Meaning k = r mod n. The division used \nduring the reduction estimates a factor q_e by dividing the upper two \ndigits (a digit having e.g. a size of 8 byte) of r by the upper digit of \nn and then decrements q_e in a loop until it has the correct size. \nObserving the number of times q_e is decremented through a control-flow \nrevealing side-channel reveals a bias in the most significant bits of \nk. Depending on the curve this is either a negligible bias or a \nsignificant bias large enough to reconstruct k with lattice reduction \nmethods. For SECP160R1, e.g., we find a bias of 15 bits."
}
],
"impacts": [
{
"descriptions": [
{
"lang": "en",
"value": "Information Disclosure"
}
]
}
],
"metrics": [
{
"cvssV3_1": {
"attackComplexity": "HIGH",
"attackVector": "LOCAL",
"availabilityImpact": "NONE",
"baseScore": 4.1,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "HIGH",
"integrityImpact": "NONE",
"privilegesRequired": "HIGH",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:L/AC:H/PR:H/UI:N/S:U/C:H/I:N/A:N",
"version": "3.1"
},
"format": "CVSS",
"scenarios": [
{
"lang": "en",
"value": "GENERAL"
}
]
}
],
"problemTypes": [
{
"descriptions": [
{
"cweId": "CWE-203",
"description": "CWE-203 Observable Discrepancy",
"lang": "en",
"type": "CWE"
}
]
}
],
"providerMetadata": {
"dateUpdated": "2026-01-27T21:58:42.146Z",
"orgId": "50d2cd11-d01a-48ed-9441-5bfce9d63b27",
"shortName": "wolfSSL"
},
"references": [
{
"url": "https://github.com/wolfSSL/wolfssl/releases/tag/v5.7.2-stable"
},
{
"url": "https://github.com/wolfSSL/wolfssl/pull/7020"
}
],
"source": {
"discovery": "EXTERNAL"
},
"title": "ECDSA nonce bias caused by truncation",
"x_generator": {
"engine": "Vulnogram 0.1.0-dev"
}
}
},
"cveMetadata": {
"assignerOrgId": "50d2cd11-d01a-48ed-9441-5bfce9d63b27",
"assignerShortName": "wolfSSL",
"cveId": "CVE-2024-1544",
"datePublished": "2024-08-27T18:44:52.770Z",
"dateReserved": "2024-02-15T17:38:16.603Z",
"dateUpdated": "2026-01-27T21:58:42.146Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.2"
}
}
}
Loading…
Loading…
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
Loading timeline…
Loading…
Loading…
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.
Loading…
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.