A curated collection of APT adversary simulation campaigns built on the custom BEAR-C2 framework, letting authorized red teams emulate Russian, Chinese, North Korean, and Iranian threat actor behavior for defensive validation.
| Tool | S3N4T0R-0X0/APTs-Adversary-Simulation — a library of APT campaign simulations modeled on real state-sponsored TTPs |
| Category | Adversary emulation / red team operations |
| Primary Use | Replaying documented APT group behavior via BEAR-C2 inside authorized lab and engagement environments to test detections against MITRE ATT&CK techniques |
| Safe Use | Strictly for authorized penetration tests, isolated lab ranges, purple-team exercises, and defensive research; the README itself carries a caution that unauthorized use may violate laws |
| Telemetry Note | Simulated campaigns are designed to mirror real attacker TTPs, so every execution generates endpoint, network, and C2-channel telemetry that EDR, SIEM, and NDR tooling should be tuned to catch — the telemetry trail is the point of the exercise |
Adversary emulation has matured from a boutique red-team exercise into a measurable discipline, and S3N4T0R-0X0/APTs-Adversary-Simulation is one of the more ambitious open attempts to package it. The repository describes itself as a comprehensive collection of simulated Advanced Persistent Threat campaigns built from the real-world tactics, techniques, and procedures of state-sponsored groups attributed to Russia, China, Iran, and North Korea. What distinguishes it from a simple list of atomic tests is that each simulation is designed as a full campaign — the README references custom tools, command and control servers, backdoors, exploitation techniques, stagers, and bootloaders intended to mirror artifacts observed in actual intrusions. With roughly 1,100 stars and a codebase flagged primarily as C++, it has clearly attracted attention from the offensive security community.
The intellectual grounding of the project is worth pausing on, because it shapes how a professional should read the material. The README cites research from well-established vendors — Palo Alto Unit42, Kaspersky, Microsoft, Cisco, Trellix, CrowdStrike, and WithSecure — as the source material for the TTPs being reproduced. That matters for fidelity: an emulation exercise is only as valuable as the accuracy of the behavior it replays, and anchoring each campaign in published incident reports means defenders can trace why a given technique appears in the simulation. The author also links a set of companion essays on Medium distinguishing adversary simulation from adversary emulation and arguing that simulation is an outcome of experience rather than a methodology, which signals a deliberate, research-driven posture rather than a spray of weaponized scripts.
Structurally, the repository organizes its content by country of origin, with vendor-standard naming conventions used throughout. On the Russian side, the completed simulations (marked with checkmarks) include Cozy Bear (APT29), Fancy Bear (APT28), Voodoo Bear, Energetic Bear, Berserk Bear, Gossamer Bear, Primitive Bear, Ember Bear, and Venomous Bear. The naming caveat in the README is a useful touch for practitioners: the same actor carries different names across Mandiant, CrowdStrike, Microsoft, and Kaspersky taxonomies, and conflating them is a classic analytical error when mapping detections back to threat intelligence reports.
The Chinese table follows the CrowdStrike panda convention, with completed entries for Mustang Panda and Wicked Panda, and planned or in-progress slots for Glacial Panda, Goblin Panda, Anchor Panda, Deep Panda, Samurai Panda, Phantom Panda, Sunrise Panda, and Ethereal Panda. The North Korean section leans on the CrowdStrike chollima lineage — Labyrinth Chollima, Velvet Chollima, Famous Chollima, Stardust Chollima, Ricochet Chollima, and Silent Chollima are all marked complete. Iran is represented through the kitten families, with Helix Kitten, Static Kitten, and Muddy Water finished and entries such as Pioneer Kitten, Clever Kitten, Tracer Kitten, Nemesis Kitten, Pulsar Kitten, Remix Kitten, and Haywire Kitten listed on the roadmap. The breadth alone makes the repo a useful index of which actors a mature program should consider covering.
The engine underneath all of this is BEAR-C2, the author's custom command and control framework, which lives in a separate repository and is referenced directly from the simulations. The GitHub topics — bear-c2, adversary-emulation, adversary-simulation, apt — confirm that the framework is the common execution substrate. Architecturally this mirrors how commercial emulation platforms work: a C2 layer that supports realistic callback behavior, plus per-actor scenario content that encodes the TTP sequence. From a defender's perspective, the fact that everything routes through a known framework is actually convenient, because it makes the simulation observable and attributable within a lab even while the individual techniques mimic real actors.
For an authorized engagement workflow, the practical value of a project like this is in detection validation rather than pure offense. A purple team can select an actor relevant to the organization's threat model — a utility company might prioritize Energetic Bear, a defense contractor Fancy Bear — walk through the documented technique chain in a controlled range, and compare what the EDR and SIEM actually alerted on against what the vendor research says the real actor does. The gaps that fall out of that exercise are directly actionable backlog items for detection engineering. That is the standard emulation loop, and having pre-built, threat-intel-anchored campaign content lowers the cost of running it significantly.
There are caveats a senior operator should weigh before pulling this into an environment. The license field reads NOASSERTION, which means there is no clear grant of usage rights — a real consideration for commercial teams whose legal departments care about provenance. The repository also links out to several sibling repositories for individual actor simulations, so the quality and completeness across campaigns may vary, and the checkmark system in the README is the only maturity signal available. As with any community-driven offensive content, each component should be reviewed in a sandbox before it touches anything resembling production infrastructure, both for safety and to understand exactly what telemetry it will emit.
The author's framing is notably defensive-friendly for a project of this type. A prominent caution block states the project is strictly for educational, research, and defensive security purposes and warns that unauthorized use may violate laws, and the accompanying essays argue for simulation as a way to build institutional experience against advanced threats. None of that is a substitute for a signed rules-of-engagement document, a scoped test range, and proper deconfliction procedures, but it does position the repository in the emulation-and-training tradition rather than the crude tool-dumping corner of GitHub.
In an operational security sense, deploying BEAR-C2 in a lab means deliberately generating C2 traffic and implant-like artifacts on the network, which has monitoring implications beyond the test hosts themselves. Teams sharing a range with other business units should ensure the simulation IP space is documented, and detection rules tuned to the framework's callbacks should be validated as firing during the exercise — otherwise the simulation proves nothing. Conversely, that same visibility makes the project a good candidate for building detection content: every technique executed is an opportunity to write and test a correlation rule mapped to a MITRE ATT&CK technique ID.
Where this repository fits in the broader landscape is between lightweight frameworks like Atomic Red Team — which execute individual techniques in isolation — and full commercial emulation platforms that bundle planner and reporting layers. APTs-Adversary-Simulation bets that the valuable unit of work is the actor campaign: the ordered, context-rich sequence of behaviors that a real group uses against a specific class of target. For teams building a threat-informed defense program on a budget, that actor-centric framing, backed by published vendor research and an open C2 framework, makes it a reference worth studying even before it is ever executed anywhere.
S3N4T0R-0X0/APTs-Adversary-Simulation.Educational analysis for authorized security professionals. Use only in controlled, authorized environments.
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