When people say "python pptx" they almost always mean python-pptx, the pure-Python library for creating and updating PowerPoint .pptx files programmatically. It is the standard way to generate a deck from a data pipeline, template a monthly report, or extract text from thousands of existing slides without opening PowerPoint once. The library itself is well-behaved, but the moment you point it at files that came from outside your own system, you inherit the security concerns that come with parsing complex, zip-and-XML-based document formats. This guide covers both: how to use it, and how not to get burned.
What python-pptx actually is
The python-pptx library, created by Steve Canny, reads and writes files in the Office Open XML format that PowerPoint uses. A .pptx file is not a single binary blob — it is a ZIP archive containing a tree of XML parts describing slides, shapes, text, and relationships. python-pptx wraps all of that behind a clean object model so you work with presentations, slides, and shapes instead of raw XML.
Installation is the usual one line:
pip install python-pptx
A minimal script that builds a deck looks like this:
from pptx import Presentation
from pptx.util import Inches, Pt
prs = Presentation()
slide = prs.slides.add_slide(prs.slide_layouts[1])
slide.shapes.title.text = "Quarterly Security Review"
slide.placeholders[1].text = "Generated automatically from scan data"
prs.save("report.pptx")
The reverse direction — reading — is just as common. To pull every line of text out of an existing deck:
from pptx import Presentation
prs = Presentation("incoming.pptx")
for slide in prs.slides:
for shape in slide.shapes:
if shape.has_text_frame:
for para in shape.text_frame.paragraphs:
print("".join(run.text for run in para.runs))
That second snippet is where security enters the picture, because incoming.pptx might not be a file you created.
Why "pptx python" parsing is a security surface
Any time you parse a file format from an untrusted source, you are running your parser against input an attacker may have crafted deliberately. The pptx python workflow has three classic pitfalls, all inherited from the ZIP-plus-XML nature of the format rather than from python-pptx doing anything wrong.
XML entity expansion (billion laughs / XXE). Office Open XML is XML, and XML parsers can be tricked into expanding nested entities that balloon a few kilobytes into gigabytes of memory, or into resolving external entities that read local files or make network requests. python-pptx parses XML with lxml under the hood; lxml's defaults are safer than they used to be, but do not assume the whole pipeline is immune. If your application does any of its own XML parsing of extracted parts, configure the parser to disable entity resolution and external DTDs.
Zip bombs and decompression amplification. Because a .pptx is a ZIP archive, a maliciously constructed file can decompress to a size wildly larger than it appears on disk, exhausting memory or filling a disk. Enforce a size limit before you open the file and be wary of processing arbitrarily large uploads.
Path traversal on extraction. If your code extracts archive members to disk (python-pptx itself keeps things in memory, but surrounding glue code sometimes unzips), a member named with ../ sequences can write outside the intended directory. Validate member names before writing anything.
Handling untrusted decks safely
If you accept .pptx uploads from users, treat every file as hostile until proven otherwise. A defensive intake looks roughly like this:
import zipfile
from pptx import Presentation
MAX_BYTES = 25 * 1024 * 1024 # cap the on-disk size
MAX_UNCOMPRESSED = 250 * 1024 * 1024 # cap total decompressed size
def load_pptx_safely(path):
# Reject anything that is not a well-formed zip up front
if not zipfile.is_zipfile(path):
raise ValueError("not a valid pptx archive")
with zipfile.ZipFile(path) as zf:
total = sum(info.file_size for info in zf.infolist())
if total > MAX_UNCOMPRESSED:
raise ValueError("decompressed size exceeds limit")
for info in zf.infolist():
if info.filename.startswith("/") or ".." in info.filename:
raise ValueError("suspicious archive member path")
return Presentation(path)
Beyond the code, run this kind of processing in an isolated worker with strict memory and CPU limits and no outbound network access. If the parser does something unexpected, a locked-down sandbox is the difference between a crashed job and a compromised host. Never process user documents in the same trusted context as your application secrets.
The python-pptx library and your dependency chain
There is a subtler security angle that has nothing to do with the file format: python-pptx is itself an open-source dependency, and it brings its own dependencies (lxml, Pillow when you handle images) into your project. Each of those has its own vulnerability history. Pillow in particular has had a steady stream of image-parsing CVEs over the years, and if your report generator embeds user-supplied images, those parsers become part of your attack surface too.
The point is not to avoid the python-pptx library — it is a solid, widely used package — but to know what it drags in and to keep watch on it. This is standard software composition analysis: inventory the full transitive tree and check it against known-vulnerability data. An SCA tool will tell you when lxml or Pillow underneath your presentation code picks up a published CVE, and a platform such as Safeguard can flag that transitive exposure before it ships. Pin your versions, and update on a schedule rather than only when something breaks.
Generating decks from data without leaking it
One last practical concern: if you are auto-generating presentations from internal data, be careful what lands on the slide. It is easy to dump a raw dataframe or a full API response into a text box and accidentally include fields you did not mean to share — internal IDs, PII, secrets pulled from a config. Treat slide content the same way you treat any other output that leaves your trust boundary and filter it deliberately. The Safeguard Academy has broader guidance on handling sensitive data in automated pipelines.
FAQ
Is python-pptx safe to use?
The library itself is well-maintained and does not execute code from the files it reads — a .pptx is data, not a program, and python-pptx does not run macros. The risk comes from parsing files from untrusted sources, where malformed XML or oversized archives can cause denial of service. Cap file sizes, isolate the worker, and keep dependencies patched.
Can a malicious .pptx run code through python-pptx?
python-pptx does not evaluate macros or scripts, so a deck cannot directly execute code the way opening a macro-enabled document in PowerPoint might. The realistic threats are resource-exhaustion attacks (zip bombs, entity expansion) and vulnerabilities in the underlying XML or image parsers, not arbitrary code execution from the library itself.
Does python-pptx handle .ppt files?
No. python-pptx only works with the modern XML-based .pptx format, not the legacy binary .ppt format. To process old .ppt files you would convert them first, for example with LibreOffice in a sandboxed job.
How do I extract text from a PowerPoint with python-pptx?
Open the file with Presentation(path), iterate over prs.slides, then over each shape, and read shape.text_frame for shapes that have text. Wrap that in size and validation checks if the files come from users.