Internet-Draft CBOR Numbers July 2024
Bormann Expires 10 January 2025 [Page]
Concise Binary Object Representation Maint&Ext
Intended Status:
C. Bormann
Universität Bremen TZI

On Numbers in CBOR


The Concise Binary Object Representation (CBOR), as defined in STD 94 (RFC 8949), is a data representation format whose design goals include the possibility of extremely small code size, fairly small message size, and extensibility without the need for version negotiation.

Among the kinds of data that a data representation format needs to be able to carry, numbers have a prominent role, but also have inherent complexity that needs attention from protocol designers and implementers of CBOR libraries and of the applications that use them.

This document gives an overview over number formats available in CBOR and some notable CBOR tags registered, and it attempts to provide information about opportunities and potential pitfalls of these number formats.

This is a rather drafty initial revision, pieced together from various components, so it has a higher level of redundancy than ultimately desired.

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Table of Contents

1. Introduction

The Concise Binary Object Representation (CBOR), as defined in RFC 8949 [STD94], is a data representation format whose design goals include the possibility of extremely small code size, fairly small message size, and extensibility without the need for version negotiation.

Among the kinds of data that a data representation format needs to be able to carry, numbers have a prominent role, but also have inherent complexity that needs attention from protocol designers and implementers of CBOR libraries and of the applications that use them.

This document gives an overview over number formats available in CBOR and some notable CBOR tags registered, and it attempts to provide information about opportunities and potential pitfalls of these number formats.

It discusses CBOR representation of numbers in four main Sections:

These sections will generally address considerations such as:

1.1. Conventions and Definitions

The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and "OPTIONAL" in this document are to be interpreted as described in BCP 14 [BCP14] when, and only when, they appear in all capitals, as shown here.

Terms and definitions from [STD94], [RFC8610], and [IEEE754] apply.

2. Integer Numbers

CBOR provides representations of integer numbers in unsigned and negative forms:

The latter two forms are often called "bignums" for historical reasons, the former "basic" integers. The Concise Data Definition Language (CDDL) [RFC8610] has the types uint, nint, and int, for the ranges of values covered by major type 0, major type 1, and either of them, respectively; biguint, bignint, and bigint for the range of value covered by tag 2, tag3, and either; and unsigned and integer for a choice of either form (but interestingly no negative). As the preferred encoding for an integer chooses between major type 0/1 and tag 2/3 automatically, in practice biguint and unsigned are the same type, as are bigint and integer.

The Major type 0 numbers come in five different encoding sizes, as indicated by their initial byte: immediate ("1+0") encoding (0..23), one-byte ("1+1") (0..255), two-byte ("1+2", 0..65535), four-byte, and eight-byte. The Preferred Serialization always uses the shortest of the major type 0 encodings available for an unsigned integer. The intention is that there is no semantic difference between the major type 0 encodings, and there also is no semantic difference between major type 0 and tag 2. This means that Preferred Serialization always uses major type 0 over tag 2 when possible, and the shortest encoding of these (and thus no leading zero bytes for the tagged encodings). Major type 1 and tag 3 are analogous.

Note that there is no "signed type" in CBOR: as any specific number to be represented is either negative or not, it is represented as an unsigned integer or as a negative integer. Major type 0 unsigned integers cover exactly the range of platform types such as uint64_t or u64. Signed platform types such as int64_t or i64 can be represented in the lower half of the unsigned space and the upper half of the negative space. Platforms typically have no nint64_t type that could take all negative numbers representable in major type 1; generic decoders will therefore treat the lower half of the negative space in the same way they will treat bignums that do not fit the signed platform type. Similarly, generic encoders for a platform with u128/i128 types will choose between major type 0/1 and tag 2/3 just like they would choose between the encoding sizes inside major type 0/1.

While additional representation of integers could be developed, the options already provided by [STD94] should be able to satisfy most applications.

3. IEEE 754 Floating Point Numbers

While integer numbers are relatively easy to represent, floating point numbers as a realization of rational or real numbers are a much more varied subject. Many rational or real numbers require rounding until they can be encoded as a floating point number.

There are many choices that can be made when designing a machine representation for floating point numbers. After decades of vendor-specific formats, IEEE standardized [IEEE754], initially in 1985, updated in 2008 and then 2019 (IEC 559 is then mirroring IEEE 754). This standard is widely adopted in hardware and software, offering choices such as binary vs. decimal floating point numbers, and different representation sizes. Out of the large choice available, CBOR directly supports the three formats binary16, binary32, and binary64, i.e., the signed binary floating point formats in 16, 32, and 64 bits, colloquially known as half (16 bits), single (32 bits), and double (64 bits) precision. Most platforms that support floating point computation support at least single precision, except for the most constrained ones also double precision, while half precision is mostly used for storage and interchange only and may be software-supported only.

3.1. Integer vs. Floating Point

Mathematically speaking, integer numbers are a subset of the rational or real numbers from which floating point numbers are drawn. In many programming environments, however, integer numbers are clearly separated from floating point numbers (the most notable exception being the original JavaScript language, which only had one number type).

For specific applications, it may be desirable to represent all numbers that can be represented as integers as such, even if they are used where floating point numbers are used for non-integers. [I-D.mcnally-deterministic-cbor] defines a CDE application profile that enforces this for a certain subset of the integers.

Most CBOR applications so far have tended to get by with the kind of strong separation between the integer and floating point worlds that programming environments usually favor, so our focus will not be on approaches for intermingling them in this document.

3.2. Considerations for non-finite numbers and non-numbers

IEEE754 distinguishes three kinds of floating point data item:

  • finite floating-point number: A finite number that is representable in a floating-point format. Note that these further divide into zero, subnormal, and normal; this distinction is usually not of interest in interchange, except that there are a few platforms with limited floating point support that may not support subnormal numbers.

  • infinite floating-point number: One of the two values −Infinite and (positive) Infinite. On many platforms, infinite numbers can be accessed via a floating point operation such as 1.0/0.0 (positive infinity) or −1.0/0.0 (negative infinity); they react to comparisons as one would expect.

  • NaN: a floating point datum that is not a number (NaN), used to represent computations that didn't lead to a numeric result, not even an infinity. A commonly implemented example for such a computation is 0.0/0.0. The formats provide a way to include additional information with a NaN, such as its sign bit, whether operations on the NaN are intended to fail immediately (signaling) or just return another NaN (quiet), and some remaining bits that may carry additional information (intended as diagnostic).

    It can be surprising that according to [IEEE754], NaN values always compare as different even if they have the same NaN information (i.e., are identical). (There is also a totalorder relation that does give NaNs a defined place, depending on their sign bits; this only recently has been standardized as part of std::strong_order in C++20 [Cplusplus20].)

Not all platforms that can use IEEE 754 do provide all these kinds, e.g., Erlang only provides finite floating-point numbers. Platforms that do provide them widely vary in the way they provide access to non-finite numbers and NaNs beyond the floating point operations given above. Usually there is an operation such as isnan() in C, which is needed as comparison to a NaN always yields inequality.

3.2.1. Protocol Design Considerations

CBOR supports the interchange of all kinds of IEEE 754 data items, including non-finite numbers and non-numbers (NaNs). For an application developer that is already using IEEE 754 floating point, there is little additional consideration required: Both infinities and NaN are widely supported in IEEE-754 hardware and software by CPUs, OS’s and programming environments. CBOR protocol designs can generally rely on infinities and NaN as a concept being supported, but implementations may run into dark corners of their platforms when it comes to distinguishing and preserving NaN information in NaN values.

However, for a protocol that wants to achieve good interoperability over a wide variety of platforms, the fact that platforms differ in their support of non-finite numbers and NaNs becomes relevant. (See Section 3.2.2 below for reasons for such differences.) Protocol designs aiming for the widest possible platform support may want to implement replacements for infinite numbers and NaNs, or at least not rely on NaN information being successfully preserved during interchange.

JSON Compatibility

Note that JSON supports neither infinite numbers nor NaN. For protocols that are intended to work in both CBOR and JSON representations and need an out-of-band indicator comparable to NaN, a protocol developer might consider this (in CDDL, where float is not intended to be a NaN value):

float-with-null = float / null

Additional choices can be added for the infinities (e.g., false and true, to stay within the CBOR simple values), if required.

Since null, false and true have single-byte representations, the replacement of NaN, −Infinity, and (positive) Infinity by these values can save bytes even if JSON compatibility is not a consideration.

Applications that need to preserve the information in a NaN (sign bit, quiet bit, payload) may want to replace null with an application-oriented representation of that information, or simply with a (left-aligned, truncating trailing zero bytes) byte string representing those bits:

float-with-nan-replacement = float / bytes

For JSON, the byte string can be base16- or base64-encoded, or it can be represented by an integer, preserving its left-aligned nature, or even as a (tagged) floating point value with a different exponent.

3.2.2. Implementation Considerations

All floating-point numbers, including zeros and infinities, are signed. A NaN also carries a sign bit. Each of the three formats binary16, binary32, and binary64 define a fixed assignment of bits in the representation towards the sign bit, an exponent, and a "significand" (which represents the mantissa, with details sometimes depending on the specific exponent value).

Table 1: Bit Allocation in Floating Point Formats
Format Sign bit Exponent Significand
binary16 1 5 10
binary32 1 8 23
binary64 1 11 52

Infinite numbers are represented in each format choice with a sign bit, the highest available exponent value (all ones) and all-zero significand. NaN values are represented with a sign bit, the highest available exponent value (all ones) and a non-zero significand, which carries a leading quiet bit with the rest of the bits allocated to the NaN payload.

To qualify as a generic encoder or decoder, a CBOR library needs to implement as much of [IEEE754] support as reasonably possible on the platform it addresses. What is reasonably possible depends on:

  • platform support for [IEEE754] numbers. If there is no such support, the generic decoder may need to resort to offering the interchanged value to the application, suitably tagged.

  • If there is partial support, it may be harder to find a good solution. This is specifically a problem for platform support that works well in most cases, but exhibits some dark corners. E.g., the implementation may support a single NaN value consistently, but not preserve NaN information present in the NaN values.

Where an implementation needs to convert between different floating point formats, e.g., because not all formats are fully supported by the platform, or to implement Preferred Serialization (as needed for Common Deterministic Encoding [I-D.ietf-cbor-cde]) in an encoder, conversion of NaNs in these formats is best done by operating on the bit patterns of the [IEEE754] number in the following way:

  • Expansion (towards a larger size format):

    • preserve the sign bit

    • expand the (all-ones) exponent to the larger (all-ones) exponent

    • fill up the significand with zero bits on the right

  • Contraction (towards a smaller size format):

    • preserve the sign bit

    • truncate the (all-ones) exponent to the smaller (all-ones) exponent

    • truncate the significand from the right; check if the removed bits were all zero.

If the contraction is optional, e.g., for Preferred Serialization, do not perform the contraction if the removed bits in the significand truncation aren't all zero. If the contraction is required to fit into limited platform types (e.g., binary32 only), a failed truncation check indicates the loss of information and should be signaled to the application. We say a contraction "preserves the NaN information" if subsequent expansion to the original size format recreates the exact same NaN value.

Appendix A.1 gives additional detailed considerations for implementations that aspire to provide full support for NaNs, preserving NaN information.

4. Other Floating Point Numbers

RFC 8949 [STD94] also defines tags 4 and 5 for a representation of decimal and binary floating point numbers that is not constrained by the types provided by IEEE 754. These tags are very flexible, but this flexibility comes with a choice of ways they could be integrated into a generic encoder. Because of this flexibility, tags 4 and 5 do not define a Preferred Serialization or a deterministic encoding.

Section 3.2 of [I-D.ietf-cbor-time-tag] uses representations derived from the tags 4 and 5 to represent timestamps. Section 6.1 of [I-D.ietf-cbor-time-tag] lists various other tags that can be used for representing numbers for advanced arithmetic, including rational numbers in fraction form (tag 30).

5. Tagged Arrays of Numbers

[RFC8746] defines tags for typed arrays, i.e., arrays of numbers that all are represented in the same way. The choices defined in the [RFC8746] are all based on traditional platform number representations (unsigned integers, signed integers, IEEE 754 floating point values) and even come in little-endian and big-endian variants, often removing the need to convert the numbers from an internal to an interchange form. As conversion for interchange is not envisioned, considerations for a preferred serialization are not applicable. As the recipient may need a conversion for ingestion of the arrays, some considerations from Section 3 may apply.

6. Security Considerations

The general security considerations for representing data in common data representation formats apply, e.g., those in Section 10 of RFC 8949 [STD94].


7. IANA Considerations


Add nan'' registration when that is ready)

8. References

8.1. Normative References

Best Current Practice 14, <>.
At the time of writing, this BCP comprises the following:
Bradner, S., "Key words for use in RFCs to Indicate Requirement Levels", BCP 14, RFC 2119, DOI 10.17487/RFC2119, , <>.
Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC 2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174, , <>.
IEEE, "IEEE Standard for Floating-Point Arithmetic", IEEE Std 754-2019, DOI 10.1109/IEEESTD.2019.8766229, <>.
Birkholz, H., Vigano, C., and C. Bormann, "Concise Data Definition Language (CDDL): A Notational Convention to Express Concise Binary Object Representation (CBOR) and JSON Data Structures", RFC 8610, DOI 10.17487/RFC8610, , <>.
Bormann, C., Ed., "Concise Binary Object Representation (CBOR) Tags for Typed Arrays", RFC 8746, DOI 10.17487/RFC8746, , <>.
Internet Standard 94, <>.
At the time of writing, this STD comprises the following:
Bormann, C. and P. Hoffman, "Concise Binary Object Representation (CBOR)", STD 94, RFC 8949, DOI 10.17487/RFC8949, , <>.

8.2. Informative References

International Organization for Standardization, "Programming languages - C++", Sixth Edition, ISO/IEC ISO/IEC JTC1 SC22 WG21 N 4860, , <>.
Bormann, C., "CBOR: On Deterministic Encoding", Work in Progress, Internet-Draft, draft-bormann-cbor-det-02, , <>.
Bormann, C., "CBOR Common Deterministic Encoding (CDE)", Work in Progress, Internet-Draft, draft-ietf-cbor-cde-02, , <>.
Bormann, C., Gamari, B., and H. Birkholz, "Concise Binary Object Representation (CBOR) Tags for Time, Duration, and Period", Work in Progress, Internet-Draft, draft-ietf-cbor-time-tag-12, , <>.
McNally, W., Allen, C., and C. Bormann, "dCBOR: A Deterministic CBOR Application Profile", Work in Progress, Internet-Draft, draft-mcnally-deterministic-cbor-10, , <>.

Appendix A. Implementers' Checklists for Floating Point Values

This check list employs [BCP14] keywords to indicate interoperability requirements on implementations.

The following considerations apply to encoding (emitting) floating point values in a generic encoder:

The following considerations apply to decoding (ingesting) floating point values in a generic decoder that supports IEEE 754 floating-point numbers:

A.1. NaN Payloads

An IEEE-754 data item has up to 52 bits in the significand. For a NaN, the first of these bits is used to indicate whether the NaN is signalling (0) or quiet (1). The up to 51 bits in the rest of the significand are called the "NAN payload".

The payload’s original purpose is diagnostic information to explain why a NaN was generated by a local computation. There is no standard for the contents of a NaN payload.

CBOR allows NaNs with non-zero payloads to be encoded. (Due to the way infinite numbers are encoded in [IEEE754], zero-payload NaN always must be quiet NaNs.)

As a result, if a protocol design does not use NaNs with non-zero payloads and is using preferred serialization then NaN must be encoded as a half-precision with the quiet bit set and the payload set as 0, specifically 0xF97E00. If a design does not use NaNs with non-zero payloads and preferred serialization is not used, then the single and double precision quiet NaNs, 0xFA7FC00000 and 0xFB7FF0000000000000, may also be used.

NaN payloads have been in the IEEE-754 standard since 2008, but programming environments often still do not provide facilities (e.g., APIs) to make use of them. For example, in C there is the isnan() API to check if a value is a NaN, but there are no APIs to construct or access the NaN payload. The typical way to work with a NaN payload is to reinterpret the floating-point value as an unsigned integer and then use shifts and masks to unpack the IEEE-754 representation.

A.1.1. NaN Implementation Details

This section is primarily for CBOR library implementors.

CBOR attempts to limit the MUSTs about CBOR implementations in order to allow its use in a large variety of constrained use cases. For example, support for integers is not required because a protocol might need only strings. Similarly, there is no MUST that requires support of NaN and NaNs with non-zero payloads, but the recommendation here is that any generic CBOR library that supports floating-point support NaNs, preferably also with non-zero NaN payloads.

In most environments, there is little extra work to do to support NaN without payloads if floating-point is supported. NaNs will usually flow through as any other floating-point value.

Generic CBOR libraries are expected to support preferred serialization of floating-point including NaNs. For NaNs with zero payloads, this requires reducing to a half-precision NaN without a payload. This requires a few explicit extra lines of code. See the sample half-precision implementation in Appendix D of RFC 8949.

The implementation of preferred serialization of NaN payloads needs a few more additional lines. As with preferred serialization, NaN payloads must be reduced but only if they can be reduced without the loss of any non-zero payload bits. Programming platform provided floating-point hardware and software may or may not do this correctly for double to single conversion. The sample half-precision implementation in Appendix D of RFC 8949 only supports NaNs without payloads.

A double precision NaN payload contains 51 bits, a single 22 bits and a half 9 bits, in each case all but the first bit of the significand. A double precision NaN can be reduced to a single precision NaN only if the right-most 29 payload bits are zero. A single precision NaN can be reduced to a half precision NaN only if the right-most 13 payload bits are zero. A double NaN can be reduced to a half precision NaN only if the right-most 42 payload bits are zero. Note that the exponent is always all-ones for NaN, so this is simpler than the equivalent contraction of regular, non-NAN, floating-point values.

To implement the above, most CBOR libraries will have to reinterpret the floating point value as an unsigned integer and use shifts and masks, based in the internal representation defined in [IEEE754].

Testing on some CPUs has shown them to do this correctly for conversion between single and double. However, it may not be very useful to rely on platform libraries for the following reasons. First, they may provide no support at all for half-precision and half-precision is required for preferred serialization. Second, NaN payloads are a relatively recent and very specialist feature that is not usually used in interchange.

If platform implementation is relied upon, NaN payload reduction should be tested on each platform. Open source libraries intended to run on multiple platforms may be better off not relying on the platform.

A.1.2. NaN Tests Examples

The IEEE-754 numbers are given as a 64-bit (binary64) or 32-bit (binary32) unsigned integer in hex to show the bits that make up the floating-point value. All of the following are NaNs.

Table 2: Examples for Preferred Serialization of NaN values
IEEE-754 Number CBOR Preferred Serialization Comment
0x7ff8000000000000 0xf97e00 qNaN contracted from double to half
0x7ff8000000000001 0xfb7ff8000000000001 Can't be contracted because of bit set in right-side part of payload
0x7ffffc0000000000 0xf97fff 10-bit payload that can be contracted to half
0x7ff80000000003ff 0xfb7ff80000000003ff right-justified payload can't be contracted
0x7fffffffe0000000 0xfa7fffffff 23-bit payload that reduces to single
0x7ffffffff0000000 0xfb7ffffffff0000000 24-bit payload that can't be contracted
0x7fffffffffffffff 0xfb7fffffffffffffff All payload bits set, can't be contracted
0x7fc00000 0xf97e00 qNaN contracted from single to half
0x7fffe000 0xf97fff single 10-bit payload that can be contracted
0x7fbff000 0xfa7fbff000 single payload that can't be contracted to 10 bits



Laurence Lundblade
Security Theory LLC

Laurence wrote much of the initial text about NaN processing.

Author's Address

Carsten Bormann
Universität Bremen TZI
Postfach 330440
D-28359 Bremen