Add .sha1 and .sha256 properties to PublicKeyInfo that calculate over the DER-encoded contents of the public_key field
2 files changed
tree: 6738fa3879b0c4356d8bceca6f16278a16b72987
  1. asn1crypto/
  2. dev/
  3. docs/
  4. tests/
  5. .gitignore
  6. .pylintrc
  7. changelog.md
  8. LICENSE
  9. readme.md
  10. run.py
  11. setup.py
readme.md

asn1crypto

A fast, pure Python library for parsing and serializing ASN.1 structures. In addition to an ASN.1 BER/DER decoder and DER serializer, the project includes a bunch of ASN.1 structures for use with various common cryptography standards:

StandardModuleSource
X509asn1crypto.x509RFC5280
CRLasn1crypto.crlRFC5280
CSRasn1crypto.csrRFC2986, RFC2985
OCSPasn1crypto.ocspRFC6960
PKCS#12asn1crypto.pkcs12RFC7292
PKCS#8asn1crypto.keysRFC5208
PKCS#1 v2.1 (RSA keys)asn1crypto.keysRFC3447
DSA keysasn1crypto.keysRFC3279
Elliptic curve keysasn1crypto.keysSECG SEC1 V2
PKCS#5 v2.1asn1crypto.algosPKCS#5 v2.1
CMS (and PKCS#7)asn1crypto.cmsRFC5652, RFC2315
TSPasn1crypto.tspRFC3161
PDF signaturesasn1crypto.pdfPDF 1.7

License

asn1crypto is licensed under the terms of the MIT license. See the LICENSE file for the exact license text.

Dependencies

Python 2.7, 3.3, 3.4, pypy or pypy3. No third-party packages required.

Version

0.9.0 - changelog

Installation

pip install asn1crypto

Documentation

The documentation for asn1crypto is composed of tutorials on basic usage and links to the source for the various pre-defined type classes.

Tutorials

Reference

Development

The following commands will run the test suite, linter and test coverage:

python run.py tests
python run.py lint
python run.py coverage

To run only some tests, pass a regular expression as a parameter to tests.

python run.py tests ocsp

Why Another Python ASN.1 Library?

Python has long had the pyasn1 and pyasn1_modules available for parsing and serializing ASN.1 structures. While the project does include a comprehensive set of tools for parsing and serializing, the performance of the library can be very poor, especially when dealing with bit fields and parsing large structures such as CRLs.

After spending extensive time using pyasn1, the following issues were identified:

  1. Poor performance
  2. Verbose, non-pythonic API
  3. Out-dated and incomplete definitions in pyasn1-modules
  4. No simple way to map data to native Python data structures
  5. No mechanism for overriden universal ASN.1 types

The pyasn1 API is largely method driven, and uses extensive configuration objects and lowerCamelCase names. There were no consistent options for converting types of native Python data structures. Since the project supports out-dated versions of Python, many newer language features are unavailable for use.

Time was spent trying to profile issues with the performance, however the architecture made it hard to pin down the primary source of the poor performance. Attempts were made to improve performance by utilizing unreleased patches and delaying parsing using the Any type. Even with such changes, the performance was still unacceptably slow.

Finally, a number of structures in the cryptographic space use universal data types such as BitString and OctetString, but interpret the data as other types. For instance, signatures are really byte strings, but are encoded as BitString. Elliptic curve keys use both BitString and OctetString to represent integers. Parsing these structures as the base universal types and then re-interpreting them wastes computation.

asn1crypto uses the following techniques to improve performance, especially when extracting one or two fields from large, complex structures:

  • Delayed parsing of byte string values
  • Persistence of original ASN.1 encoded data until a value is changed
  • Lazy loading of child fields
  • Utilization of high-level Python stdlib modules

While there is no extensive performance test suite, the CRLTests.test_parse_crl test case was used to parse a 21MB CRL file on a late 2013 rMBP. asn1crypto parsed the certificate serial numbers in just under 8 seconds. With pyasn1, using definitions from pyasn1-modules, the same parsing took over 4,100 seconds.

For smaller structures the performance difference can range from a few times faster to an order of magnitude of more.