As reported by BleepingComputer, a months-long campaign has been quietly targeting Python developers who build Telegram bots using the popular Pyrogram library — distributing trojanized forks through PyPI designed to hand attackers persistent access to victim servers.

Threat Alert: As reported by BleepingComputer, a months-long campaign has been quietly targeting Python developers who build Telegram bots using the popular Pyrogram library — distributing trojanized forks through PyPI designed to hand attackers persistent access to victim servers.

Why This Attack Is More Dangerous Than It Looks

On the surface, "arbitrary file read" sounds like a limited capability. In practice, it's often the first step toward total server compromise. On a Telegram bot server, arbitrary file read can expose:

Why This Attack Is More Dangerous Than It Looks
Bot API tokens — allowing an attacker to fully impersonate or hijack the bot
Environment files (.env) — which commonly contain database credentials, third-party API keys, and cloud service tokens
SSH private keys — enabling lateral movement to other infrastructure
Session files and cookies — particularly dangerous given Pyrogram's use of Telegram session strings for authentication

In the Telegram bot ecosystem specifically, a compromised bot token can mean full read/write access to every conversation the bot participates in — including private messages across potentially thousands of users. This elevates the downstream blast radius well beyond the developer's own infrastructure.

The Broader Supply Chain Threat to Niche Ecosystems

What makes this campaign tactically sophisticated is its targeting. Rather than attacking mainstream libraries with millions of downloads — where malicious packages are more likely to be spotted quickly — the attackers went after a specialized developer niche. Pyrogram has a loyal but relatively small user base of developers building Telegram bots, automation scripts, and userbot clients.

This is a deliberate strategy: niche libraries receive less scrutiny from automated scanning tools, security researchers, and the broader community. A trojanized fork of a major framework like requests or Flask would likely be flagged within hours. A convincing Pyrogram clone can persist for months — as this campaign demonstrates, having been active since at least November.

Attackers are increasingly aware that the highest-value, lowest-risk targets in open source supply chains are not the most popular packages — they are the most trusted ones within a specific community.

Developer Trust as an Attack Surface

Developers installing Pyrogram forks are often doing so intentionally — seeking features, patches, or async support variants not available in the canonical package. This means traditional "don't install unknown packages" guidance doesn't fully apply here. The social engineering is baked into the ecosystem itself: forks, community variants, and unofficial builds are a normal part of the Python/Telegram bot development workflow.

Attackers exploit this by crafting packages that appear to be legitimate community forks — complete with plausible versioning, similar naming conventions, and sometimes even functional code that mirrors the original library's behavior while silently executing malicious payloads.

Shield53 Recommendations

For Developers Building Telegram Bots

  • Audit your requirements.txt and pyproject.toml immediately. Verify every Pyrogram-related package name against the canonical PyPI listing and the official GitHub repository.
  • Pin exact versions with hash verification. Use pip install --require-hashes with a locked requirements.txt to prevent silent package substitution.
  • Rotate all credentials stored on affected servers — especially bot tokens, API keys, database passwords, and any Telegram session strings. Treat them as fully compromised.
  • Inspect running environments for unexpected outbound connections using tools like netstat, ss, or EDR telemetry.

For Security Teams and Platform Defenders

  • Implement Software Composition Analysis (SCA) in CI/CD pipelines. Tools like pip-audit, Snyk, or Socket.dev can flag suspicious package behaviors and newly published variants.
  • Monitor PyPI installations in build environments for packages deviating from approved dependency lists — treat any unapproved fork as high-risk until verified.
  • Alert on file read activity targeting sensitive paths (/etc/passwd, ~/.ssh/, .env files) from Python processes in production environments.
  • Establish a private package mirror or allowlist for production deployments to prevent dependency confusion attacks from resolving to malicious public packages.

Longer-Term Posture

Organizations that allow developers to consume open source packages directly from public registries without a vetting layer are operating with significant blind spots. This campaign is a reminder that supply chain hygiene is not a one-time audit — it requires continuous monitoring, especially for packages in active development workflows. Investing in a dependency management platform with real-time threat intelligence feeds is no longer optional for teams building production services on open source foundations.