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Malicious PyPI packages can conceal their behavior in compiled .pyc files while leaving their visible Python source to act as a loader. In a June 2023 case, ReversingLabs reported that fshec2 used this approach: its source looked benign, but its compiled payload collected system information and supported further execution. The case shows why reviewing source files alone can miss behavior in an installed package—not that compiled Python files are inherently malicious.
How the fshec2 package concealed its payload
ReversingLabs published its account of fshec2 on June 1, 2023. The company said it reported the package to PyPI on April 17, 2023, and PyPI removed it that day. The report described three files in the package: _init_.py, main.py and full.pyc. The first two appeared benign when inspected as source; the compiled file held the malicious functionality. ReversingLabs’ incident report
The package entry point imported a function from main.py. That file used Python’s importlib machinery to load the compiled module rather than importing it through the ordinary import directive. ReversingLabs considered this choice consistent with an attempt to avoid detection, while noting that the usual import mechanism could have worked. The important point is the execution path: a small, innocuous-looking loader can lead to code that is not visible in a source-only review.
What the compiled file did
After decompiling full.pyc, ReversingLabs found a get_path method that collected usernames, hostnames and directory listings. Its analysis also identified IP-based URLs, process creation and file execution. These are findings from the researchers’ examination of this particular package, not characteristics of compiled Python files generally.
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ReversingLabs said files exposed by a misconfigured command-and-control (C2) host confirmed that developers had installed the package and that machine names, usernames and directory listings had been harvested. The report described at least two infected targets, but said the researchers could not identify them or establish who was behind the attack. Its indicators of compromise—including two SHA-1 hashes for version 1.0.0 and a C2 server address—are historical investigation artifacts; the report does not establish that the infrastructure remains live or that the package is currently available.
Why source-only review can miss compiled Python behavior
Python packages can contain readable .py files, compiled .pyc bytecode, or native executables produced from Python with tools such as PyInstaller. In fshec2, the visible source served as a loader and the compiled Python file contained the concealed behavior. ReversingLabs said a source-only check could miss the payload and that decompilation was needed to expose it.
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This creates an inspection-execution gap: the files a reviewer reads may not fully represent what the installed artifact executes. It is a reason to inspect package contents and loading behavior, not to treat every compiled file as suspicious. A legitimate package may include bytecode for ordinary distribution or operational reasons.
What a broader bytecode study does—and does not—show
A 2026 preprint by Baihong Chen, Tian Xie and Wen Li, Beyond Source: An Empirical Study of Python Bytecode Security Risks, examined a collected corpus of 1,034,843 PyPI artifacts. The authors identified 7,388 artifacts containing bytecode, including 228,578 .pyc files and 28,193 artifact-local .pyc files without corresponding source in the artifact. These are counts from the authors’ corpus, not a census of all current PyPI releases or a measure of how many packages are malicious. The 2026 bytecode study
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The study also tested decompilation and runtime analysis. For in-scope files targeting CPython 3.8–3.14, at least one selected decompiler emitted source for 204,901 of 204,904 files. The authors explicitly measure source emission—not proof that the output is functionally equivalent to the original bytecode. They also observed exceptions and timeouts while analyzing PyPI bytecode, as well as native process failures on adversarially mutated bytecode.
In runtime fuzzing experiments, the authors reported 1,009 stack-deduplicated findings, including 261 groups with potential memory-corruption characteristics; at least 91.7% of groups reached execution beyond a documented-unsafe ingestion boundary. Those results concern the study’s test design. The authors distinguish their runtime and source-reproduction experiments from claims that the PyPI corpus itself caused the reported crashes. They should not be read as evidence that ordinary PyPI packages commonly compromise the Python interpreter.
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How to review a Python package more completely
The following controls address different parts of the risk. None proves a package is safe: names, metadata and a linked source repository cannot by themselves establish what is inside the artifact a user installs.
- Inspect the distribution you will install. Examine the built package artifact, not only its linked repository. PyPI’s separate
aiocpaincident analysis notes that uploaded distributions and source repositories need not match exactly. PyPI’saiocpaanalysis - Include compiled and non-source contents. Inventory
.pycfiles and other packaged files. Where appropriate, use bytecode disassembly or a decompiler compatible with the relevant Python version, then validate suspicious behavior rather than treating source emission as proof of equivalence. - Trace the loading path. Follow the package entry point through import-time code and dynamic loading. Look for code that loads modules or files indirectly, then inspect the loaded content as well as the loader.
- Pin versions and use hashes where feasible. These controls can reduce exposure to unexpected package changes; PyPI recommends them in its
aiocpaanalysis. - Monitor or restrict unexpected outbound traffic. Network controls in development and build environments can limit what a package can reach if it behaves unexpectedly. PyPI’s analysis presents outbound network firewalls as an additional safeguard.
What the incident established about detection
ReversingLabs reverse engineer Karlo Zanki described the finding as “a novel attack on PyPI using compiled Python code to evade detection — possibly the first attack to take advantage of PYC file direct execution.” The word “possibly” matters: this was the researcher’s contemporaneous characterization, not an independently established priority claim. The report demonstrates a concrete way a source-focused review can miss a payload; it does not establish attribution or show how common the technique is today.
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