EPSS Exploitation Probability
EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.
Blast Radius
pyload-ngReal-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.
Description
Summary
A log injection vulnerability was identified in pyload. This vulnerability allows any unauthenticated actor to inject arbitrary messages into the logs gathered by pyload.
Details
pyload will generate a log entry when attempting to sign in with faulty credentials. This entry will be in the form of Login failed for user 'USERNAME'. However, when supplied with a username containing a newline, this newline is not properly escaped. Newlines are also the delimiter between log entries. This allows the attacker to inject new log entries into the log file.
PoC
Run pyload in the default configuration by running the following command
pyload
We can now sign in as the pyload user and view the logs at http://localhost:8000/logs.
Any unauthenticated attacker can now make the following request to inject arbitrary logs.
curl 'http://localhost:8000/login?next=http://localhost:8000/' -X POST -H 'Content-Type: application/x-www-form-urlencoded' --data-raw $'do=login&username=wrong\'%0a[2024-01-05 02:49:19] HACKER PinkDraconian THIS ENTRY HAS BEEN INJECTED&password=wrong&submit=Login'
If we now were to look at the logs again, we see that the entry has successfully been injected.
Impact
Forged or otherwise, corrupted log files can be used to cover an attacker’s tracks or even to implicate another party in the commission of a malicious act.
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | pyload-ng | all versions | 0.5.0b3.dev77 |
Research use only. For defensive security, authorized penetration testing, and academic research only. Never execute exploit code against systems without explicit written authorization.
Detection & mitigation playbook
Open-source dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for pyload-ng. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.
Fix
Update pyload-ng to 0.5.0b3.dev77 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-ghmw-rwh8-6qmr is resolved across your whole dependency graph.
Workarounds
If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.
How O3 protects you
O3 pinpoints whether GHSA-ghmw-rwh8-6qmr is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.
Tailored to GHSA-ghmw-rwh8-6qmr. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.
Frequently Asked Questions
Is GHSA-ghmw-rwh8-6qmr in your dependencies?
O3 detects GHSA-ghmw-rwh8-6qmr across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.