Validation functionality

Note

Before reading this, you may want to take a look at Factors in play when validating a signature for some background on the validation process.

General API design

PyHanko’s validation functionality resides in the validation module. Its most important components are

While you probably won’t need to interface with DocumentSecurityStore directly, knowing a little about EmbeddedPdfSignature and SignatureStatus is useful.

Accessing signatures in a document

There is a convenience property on PdfFileReader, aptly named embedded_signatures. This property produces an array of EmbeddedPdfSignature objects, in the order that they were applied to the document. The result is cached on the reader object.

These objects can be used to inspect the signature manually, if necessary, but they are mainly intended to be used as input for validation APIs.

Validating a PDF signature

All validation in pyHanko is done with respect to a certain validation context (an object of type pyhanko_certvalidator.ValidationContext). This object tells pyHanko what the trusted certificates are, and transparently provides mechanisms to request and keep track of revocation data. For LTV validation purposes, a ValidationContext can also specify a point in time at which the validation should be carried out.

Note

PyHanko currently uses a forked version of the certvalidator library, registered as pyhanko-certvalidator on PyPI. The forked version has over time diverged considerably from the original, but should be largely backwards-compatible as far as basic usage is concerned.

Originally, the principal purpose of the ValidationContext was to let the user explicitly specify their own trust settings, but ValidationContext objects are stateful: they also accumulate revocation data and validation results. It may be necessary to juggle several different validation contexts over the course of a validation operation. For example, when performing LTV validation, pyHanko will first validate the signature’s timestamp against the user-specified validation context, and then build a new validation context relative to the signing time specified in the timestamp.

Here’s a simple example to illustrate the process of validating a PDF signature w.r.t. a specific trust root.

from pyhanko.keys import load_cert_from_pemder
from pyhanko_certvalidator import ValidationContext
from pyhanko.pdf_utils.reader import PdfFileReader
from pyhanko.sign.validation import validate_pdf_signature

root_cert = load_cert_from_pemder('path/to/certfile')
vc = ValidationContext(trust_roots=[root_cert])

with open('document.pdf', 'rb') as doc:
    r = PdfFileReader(doc)
    sig = r.embedded_signatures[0]
    status = validate_pdf_signature(sig, vc)
    print(status.pretty_print_details())

Validating signatures against EU trusted lists

Added in version 0.30.0.

With the optional [etsi] dependency group installed, pyHanko also supports using EU trusted lists as trust roots. PyHanko will verify the XML signatures on the lists while collecting them–default bootstrap keys for the EU list-of-the-lists (LOTL) are bundled with the library.

import asyncio
import aiohttp
from datetime import timedelta
from pyhanko_certvalidator import ValidationContext
from pyhanko.pdf_utils.reader import PdfFileReader
from pyhanko.sign.validation import async_validate_pdf_signature
from pyhanko.sign.validation.qualified.eutl_fetch import (
    FileSystemTLCache,
    lotl_to_registry,
)
from pyhanko.sign.validation.qualified.tsp import TSPTrustManager


async def prepare_registry():
    async with aiohttp.ClientSession() as client:
        tl_cache = FileSystemTLCache(
            '/var/cache/trust-lists',
            expire_after=timedelta(days=14)
        )
        registry, errors = await lotl_to_registry(
            # 'None' => bootstrap from the list-of-the-lists
            # Note: downloading the full EUTL for all member states
            # on a cold cache can take a while
            # pass only_territories='be,fr,de' if you want to
            # limit the number of lists to take into account.
            lotl_xml=None,
            client=client,
            cache=tl_cache,
        )

        # the 'errors' are recoverable errors, they generally
        # mean that the collected data may be incomplete
        return registry


async def run():
    registry = await prepare_registry()
    trust_manager = TSPTrustManager(tsp_registry=registry)
    vc = ValidationContext(
        trust_manager=trust_manager,
        allow_fetching=True,
        revocation_mode='require'
    )

    with open('document.pdf', 'rb') as doc:
        r = PdfFileReader(doc)
        sig = r.embedded_signatures[0]
        status = await async_validate_pdf_signature(sig, vc)
        print(status.pretty_print_details())

Long-term verifiability checking

Changed in version 0.31.0: Updated to reference newer AdES-based API in pyhanko.sign.validation.ades.

As explained here and here in the CLI documentation, making sure that PDF signatures remain verifiable over long time scales requires special care. Signatures that have this property are called “LTV enabled” in some implementations, where LTV is short for long-term verifiable.

The notion of what it means to be “LTV enabled” is not entirely well-defined (since it inherently depends on the set of trust roots and policies used by the validator). One way to model this is to ask what a future validator would conclude given the validation information embedded into the document at the time of signing (assuming reasonable timestamp chain maintenance). See simulate_future_ades_lta_validation() for a standards-based attempt to formalise this.

To validate a signature while taking into account embedded historical validation data, we recommend using ades_lta_validation(). This function is part of pyHanko’s AdES validation API, which aims to implement the validation methodology laid out in ETSI EN 319 102-1. Here’s what that looks like.

from pyhanko.keys import load_cert_from_pemder
from pyhanko.pdf_utils.reader import PdfFileReader
from pyhanko.sign.validation.ades import ades_lta_validation
from pyhanko.sign.validation.policy_decl import (
    PdfSignatureValidationSpec,
    SignatureValidationSpec
)
from pyhanko_certvalidator.context import CertValidationPolicySpec
from pyhanko_certvalidator.policy_decl import REQUIRE_REVINFO
from pyhanko_certvalidator.registry import SimpleTrustManager

async def run():
    root_cert = load_cert_from_pemder('path/to/certfile')

    trust_manager = SimpleTrustManager.build(
        trust_roots=[root_cert],
    )
    validation_spec = PdfSignatureValidationSpec(
        SignatureValidationSpec(
            cert_validation_policy=CertValidationPolicySpec(
                trust_manager=trust_manager,
                revinfo_policy=REQUIRE_REVINFO,
            ),
        )
    )
    with open('document.pdf', 'rb') as doc:
        r = PdfFileReader(doc)
        sig = r.embedded_signatures[0]
        ades_status = await ades_lta_validation(
            sig, validation_spec
        )
        print(ades_status.ades_subindic)
        print(ades_status.api_status.pretty_print_details())

Notice how, rather than passing a ValidationContext object directly, the example code supplies a declarative “validation spec” instead. The AdES validator will internally create ValidationContext objects as necessary, and supply them with revocation data in accordance with the rules around proof-of-existence management.

The status object returned also includes more information than just the “regular” PdfSignatureStatus: AdESLTAValidationResult also contains some AdES-specific status codes and structured validation outputs; the pyHanko-specific PdfSignatureStatus is included as an attribute.

Incremental update analysis

Changed in version 0.2.0: The initial ad-hoc approach was replaced by a more extensible and maintainable rule-based validation system. See pyhanko.sign.diff_analysis.

As explained in the CLI documentation, the PDF standard has provisions that allow files to be updated by appending so-called “incremental updates”. This also works for signed documents, since appending data does not destroy the cryptographic integrity of the signed data.

That being said, since incremental updates can change essentially any aspect of the resulting document, validators need to be careful to evaluate whether these updates were added for a legitimate reason. Examples of such legitimate reasons could include the following:

  • adding a second signature,

  • adding comments,

  • filling in (part of) a form,

  • updating document metadata,

  • performing cryptographic “bookkeeping work” such as appending fresh document timestamps and/or revocation information to ensure the long-term verifiability of a signature.

Not all of these reasons are necessarily always valid: the signer can tell the validator which modifications they allow to go ahead without invalidating their signature. This can either be done through the “DocMDP” setting (see MDPPerm), or for form fields, more granularly using FieldMDP settings (see FieldMDPSpec).

That being said, the standard does not specify a concrete procedure for validating any of this. PyHanko takes a reject-by-default approach: the difference analysis tool uses rules to compare document revisions, and judge which object updating operations are legitimate (at a given MDPPerm level). Any modifications for which there is no justification invalidate the signature.

The default diff policy is defined in DEFAULT_DIFF_POLICY, but you can define your own, either by implementing your own subclass of DiffPolicy, or by defining your own rules and passing those to an instance of StandardDiffPolicy. StandardDiffPolicy takes care of some boilerplate for you, and is the mechanism backing DEFAULT_DIFF_POLICY. Explaining precisely how to implement custom diff rules is beyond the scope of this guide, but you can take a look at the source of the diff_analysis module for more information.

To actually use a custom diff policy, you can proceed as follows.

from pyhanko.keys import load_cert_from_pemder
from pyhanko_certvalidator import ValidationContext
from pyhanko.pdf_utils.reader import PdfFileReader
from pyhanko.sign.validation import validate_pdf_signature

from my_awesome_module import CustomDiffPolicy

root_cert = load_cert_from_pemder('path/to/certfile')
vc = ValidationContext(trust_roots=[root_cert])

with open('document.pdf', 'rb') as doc:
    r = PdfFileReader(doc)
    sig = r.embedded_signatures[0]
    status = validate_pdf_signature(sig, vc, diff_policy=CustomDiffPolicy())
    print(status.pretty_print_details())

The modification_level and docmdp_ok attributes on PdfSignatureStatus will tell you to what degree the signed file has been modified after signing (according to the diff policy used).

Warning

The most lenient MDP level, ANNOTATE, is currently not supported by the default diff policy.

Danger

Due to the lack of standardisation when it comes to signature validation, correctly adjudicating incremental updates is inherently somewhat risky and ill-defined, so until pyHanko matures, you probably shouldn’t rely on its judgments too heavily.

Should you run into unexpected results, by all means start a discussion. All information helps!

If necessary, you can opt to turn off difference analysis altogether. This is sometimes a very reasonable thing to do, e.g. in the following cases:

  • you don’t trust pyHanko to correctly evaluate the changes;

  • the (sometimes rather large) performance cost of doing the diff analysis is not worth the benefits;

  • you need validate only one signature, after which the document shouldn’t change at all.

In these cases, you might want to rely on the coverage property of PdfSignatureStatus instead. This property describes the degree to which a given signature covers a file, and is much cheaper/easier to compute.

Anyhow, to disable diff analysis completely, it suffices to pass the skip_diff parameter to validate_pdf_signature().

from pyhanko.keys import load_cert_from_pemder
from pyhanko_certvalidator import ValidationContext
from pyhanko.pdf_utils.reader import PdfFileReader
from pyhanko.sign.validation import validate_pdf_signature

root_cert = load_cert_from_pemder('path/to/certfile')
vc = ValidationContext(trust_roots=[root_cert])

with open('document.pdf', 'rb') as doc:
    r = PdfFileReader(doc)
    sig = r.embedded_signatures[0]
    status = validate_pdf_signature(sig, vc, skip_diff=True)
    print(status.pretty_print_details())

Probing different aspects of the validity of a signature

The PdfSignatureStatus objects returned by validate_pdf_signature() and other validation API functions provide a fairly granular account of the validity of the signature.

You can print a human-readable validity report by calling pretty_print_details(), and if all you’re interested in is a yes/no judgment, use the the bottom_line property.

Should you ever need to know more, a PdfSignatureStatus object also includes information on things like

  • the certificates making up the chain of trust,

  • the validity of the embedded timestamp token (if present),

  • the invasiveness of incremental updates applied after signing,

  • seed value constraint compliance.

For more information, take a look at PdfSignatureStatus in the API reference.