analyzing-cobalt-strike-beacon-configuration

analyzing-cobalt-strike-beacon-configuration is a skill for Claude Code from oyi77/1ai-skills. It costs 54 tokens per session (3,795 once invoked), scanned A, original, MIT.

A malware-analysis guide for extracting the hidden settings from Cobalt Strike Beacon files or memory dumps. Cobalt Strike is an adversary-simulation tool that is also commonly abused by attackers; its settings can reveal how the malware communicates.

In plain words
What is it for?
Use it to investigate Beacon payloads, map attacker infrastructure, understand operator choices, and create detections or threat-hunting queries.
Why use it?
The configuration can expose command-and-control servers, communication methods, timing, identifiers, and encryption details that are not obvious from the file itself.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the 1ai-skills plugin — 209 skills, 4 commands shipped together

Good fit Use it to investigate Beacon payloads, map attacker infrastructure, understand operator choices, and create detections or threat-hunting queries.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/oyi77/1ai-skills/analyzing-cobalt-strike-beacon-configuration
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add oyi77/1ai-skills --skill analyzing-cobalt-strike-beacon-configuration
Clone the repo
git clone --depth 1 https://github.com/oyi77/1ai-skills

Made for: Claude Code.

Or install 1ai-skills, the plugin that ships this one along with the rest of its 209 skills, 4 commands.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for analyzing-cobalt-strike-beacon-configuration

README.md
[![agentmods](https://agentmods.dev/badge/skills/oyi77/1ai-skills/analyzing-cobalt-strike-beacon-configuration/github.svg)](https://agentmods.dev/skills/oyi77/1ai-skills/analyzing-cobalt-strike-beacon-configuration)
Your own site
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Your own site · 80×15
<a href="https://agentmods.dev/skills/oyi77/1ai-skills/analyzing-cobalt-strike-beacon-configuration"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/analyzing-cobalt-strike-beacon-configuration.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 54 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,795 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to critical

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • critical YARA Match · line 47
    YARA rule matched a known malware signature (reverse shell, backdoor, ransomware, C2 framework, or info stealer).
    Fix: Remove the malware payload or compromised file entirely. Investigate how it entered the skill and audit all other artifacts for additional indicators of compromise.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00054 $0.03795
Opus 5 $0.00027 $0.01898
Sonnet 5 $0.00011 $0.00759
Haiku 4.5 $0.00005 $0.00380

Measured 8d ago against content hash 78d13ccf36c8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

analyzing-cobalt-strike-beacon-configuration scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

cybersecurity/analyzing-cobalt-strike-beacon-configuration/SKILL.md · 438 lines

How it starts

The opening of the file, as written. The whole thing — 438 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Analyzing Cobalt Strike Beacon Configuration

Overview

Cobalt Strike is a commercial adversary simulation tool widely abused by threat actors for post-exploitation operations. Beacon payloads contain embedded configuration data that reveals C2 server addresses, communication protocols, sleep intervals, jitter values, malleable C2 profile settings, watermark identifiers, and encryption keys. Extracting this configuration from PE files, shellcode, or memory dumps is critical for incident responders to map attacker infrastructure and attribute campaigns. The beacon configuration is XOR-encoded using a single byte (0x69 for version 3, 0x2e for version 4) and stored in a Type-Length-Value (TLV) format within the .data section.

When to Use

Trigger phrases:

  • "analyzing cobalt strike beacon configuration"

  • "Extract and analyze Cobalt Strike beacon configuration from PE files and memory "

  • When investigating security incidents that require analyzing cobalt strike beacon configuration

  • When building detection rules or threat hunting queries for this domain

  • When SOC analysts need structured procedures for this analysis type

  • When validating security monitoring coverage for related attack techniques

Prerequisites

  • Python 3.9+ with dissect.cobaltstrike, pefile, yara-python
  • SentinelOne CobaltStrikeParser (parse_beacon_config.py)
  • Hex editor (010 Editor, HxD) for manual inspection
  • Understanding of PE file format and XOR encoding
  • Memory dump acquisition tools (Volatility3, WinDbg)
  • Network analysis tools (Wireshark) for C2 traffic correlation

Key Concepts

This section covers key concepts for analyzing cobalt strike beacon configuration.

  • Ensure all prerequisites are met before proceeding
  • Follow the documented workflow steps in sequence
  • Record results and any anomalies encountered during this phase

Beacon Configuration Structure

Cobalt Strike beacons store their configuration as a blob of TLV (Type-Length-Value) entries within the .data section of the PE. Stageless beacons XOR the entire beacon code with a 4-byte key. The configuration blob itself uses a single-byte XOR key. Each TLV entry contains a 2-byte type identifier (e.g., 0x0001 for BeaconType, 0x0008 for C2Server), a 2-byte length, and variable-length data.

Read the full file on GitHub · 438 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 8d ago First seen · 438 lines · 54 tokens per session scan A 78d13ccf36c8

Subscribe to this mod's changes

analyzing-cobalt-strike-beacon-configuration is a skill published in the GitHub repository oyi77/1ai-skills (12 stars, last pushed today), licensed MIT. It adds 54 tokens to every session and 3,795 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-04.

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