Agent skill

Detecting Kerberoasting Attacks

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Detect Kerberoasting attacks by monitoring for anomalous Kerberos TGS requests (Event ID 4769) targeting service accounts with SPNs, which attackers request offline to crack service account passwords.

Apache-2.0Auto-check passedSecurity

Install Detecting Kerberoasting Attacks

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-kerberoasting-attacks -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills detecting-kerberoasting-attacks --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/detecting-kerberoasting-attacks .claude/skills/detecting-kerberoasting-attacks && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
detecting-kerberoasting-attacks
GitHub stars
34k
Token cost
~914 tokens
SKILL.md length
292 words
Files
8 (incl. scripts, references, assets)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Detect Kerberoasting attacks by monitoring for anomalous Kerberos TGS requests (Event ID 4769) targeting service accounts with SPNs, which attackers request offline to crack service account passwords.

  • Works in 7 steps: Formulate Hypothesis: Define a testable… → Identify Data Sources: Determine which… → Execute Queries: Run detection queries… → …
  • Hunting for MITRE T1558 credential access activity
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 3 more sections
  • Runs Python scripts from its folder

What it does

Detecting Kerberoasting Attacks is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Detect Kerberoasting attacks by monitoring for anomalous Kerberos TGS requests (Event ID 4769) targeting service accounts with SPNs, which attackers request offline to crack service account passwords. Use when hunting for MITRE T1558 credential access activity or investigating suspected service account password cracking attempts in Active Directory Kerberos logs.

Its SKILL.md is about 910 tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts, reference files and assets (for example `assets/template.md`, `references/api-reference.md` and `references/standards.md`).

It sits in Security, covering Red teaming and adversary simulation. The repository describes itself as: 817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io…. The licence is Apache-2.0.

When your agent uses it

  • Hunting for MITRE T1558 credential access activity
  • Investigating suspected service account password cracking attempts in Active Directory Kerberos logs

Example prompts

  • “/detecting-kerberoasting-attacks”

Requirements

  • Python 3

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Formulate Hypothesis: Define a testable hypothesis based on threat intelligence or ATT&CK gap analysis.
  2. Identify Data Sources: Determine which logs and telemetry are needed to validate or refute the hypothesis.
  3. Execute Queries: Run detection queries against SIEM and EDR platforms to collect relevant events.
  4. Analyze Results: Examine query results for anomalies, correlating across multiple data sources.
  5. Validate Findings: Distinguish true positives from false positives through contextual analysis.
  6. Correlate Activity: Link findings to broader attack chains and threat actor TTPs.
  7. Document and Report: Record findings, update detection rules, and recommend response actions.

What it can do on your machine

Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Detecting Kerberoasting Attacks loads about 914 tokens when it runs, and up to ~2.4k if it reads all its reference files. Until then it costs about 99 tokens; SKILL.md has 292 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~99
When it runs · the whole SKILL.md, loaded when a task matches
~914
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 292 words, ~914 tokens.

Download SKILL.mdSave it as .claude/skills/detecting-kerberoasting-attacks/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.
name
detecting-kerberoasting-attacks
description
Detect Kerberoasting attacks by monitoring for anomalous Kerberos TGS requests (Event ID 4769) targeting service accounts with SPNs, which attackers request offline to crack service account passwords. Use when hunting for MITRE T1558 credential access activity or investigating suspected service account password cracking attempts in Active Directory Kerberos logs.
domain
cybersecurity
subdomain
threat-hunting
tags
threat-hunting, mitre-attack, kerberoasting, credential-access, kerberos, t1558, proactive-detection
version
1.0
author
mahipal
license
Apache-2.0
d3fend_techniques
Application Protocol Command Analysis, Network Isolation, Network Traffic Analysis, Client-server Payload Profiling, Network Traffic Community Deviation
nist_csf
DE.CM-01, DE.AE-02, DE.AE-07, ID.RA-05
mitre_attack
T1046, T1057, T1082, T1083, T1003

Detecting Kerberoasting Attacks

When to Use

  • When proactively hunting for indicators of detecting kerberoasting attacks in the environment
  • After threat intelligence indicates active campaigns using these techniques
  • During incident response to scope compromise related to these techniques
  • When EDR or SIEM alerts trigger on related indicators
  • During periodic security assessments and purple team exercises

Prerequisites

  • EDR platform with process and network telemetry (CrowdStrike, MDE, SentinelOne)
  • SIEM with relevant log data ingested (Splunk, Elastic, Sentinel)
  • Sysmon deployed with comprehensive configuration
  • Windows Security Event Log forwarding enabled
  • Threat intelligence feeds for IOC correlation

Workflow

  1. Formulate Hypothesis: Define a testable hypothesis based on threat intelligence or ATT&CK gap analysis.
  2. Identify Data Sources: Determine which logs and telemetry are needed to validate or refute the hypothesis.
  3. Execute Queries: Run detection queries against SIEM and EDR platforms to collect relevant events.
  4. Analyze Results: Examine query results for anomalies, correlating across multiple data sources.
  5. Validate Findings: Distinguish true positives from false positives through contextual analysis.
  6. Correlate Activity: Link findings to broader attack chains and threat actor TTPs.
  7. Document and Report: Record findings, update detection rules, and recommend response actions.

Key Concepts

ConceptDescription
T1558.003Kerberoasting
T1558.004AS-REP Roasting
T1558.001Golden Ticket

Tools & Systems

ToolPurpose
CrowdStrike FalconEDR telemetry and threat detection
Microsoft Defender for EndpointAdvanced hunting with KQL
Splunk EnterpriseSIEM log analysis with SPL queries
Elastic SecurityDetection rules and investigation timeline
SysmonDetailed Windows event monitoring
VelociraptorEndpoint artifact collection and hunting
Sigma RulesCross-platform detection rule format

Common Scenarios

  1. Scenario 1: Rubeus kerberoast targeting all SPN accounts
  2. Scenario 2: GetUserSPNs.py from Impacket requesting RC4 tickets
  3. Scenario 3: Targeted kerberoast against high-privilege service accounts
  4. Scenario 4: AS-REP roasting accounts without pre-authentication

Output Format

Hunt ID: TH-DETECT-[DATE]-[SEQ]
Technique: T1558.003
Host: [Hostname]
User: [Account context]
Evidence: [Log entries, process trees, network data]
Risk Level: [Critical/High/Medium/Low]
Confidence: [High/Medium/Low]
Recommended Action: [Containment, investigation, monitoring]

© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 7 other files (scripts, references, assets) in skills/detecting-kerberoasting-attacks of mukul975/Anthropic-Cybersecurity-Skills.

  • SKILL.md
  • LICENSE
  • assets/template.md
  • references/api-reference.md
  • references/standards.md
  • references/workflows.md
  • scripts/agent.py
  • scripts/process.py

Open the folder on GitHubat commit 54a7988

Compare with similar skills

Detecting Kerberoasting Attacks next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Detecting Kerberoasting Attacks compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Detecting Kerberoasting Attacks this skillmukul975/Anthropic-Cybersecurity-Skills34k—~914Automated safety check: PassApache-2.0
Authorization Bypass DetectionTencent/AI-Infra-Guard6.8k—~753Automated safety check: PassApache-2.0
Run Assert Evalresponsibleai/ASSERT330—~11kAutomated safety check: NotesMIT
Osint Methodologyelementalsouls/Claude-OSINT2.8k—~8.7kAutomated safety check: NotesMIT
Lfd Designelvisun/loss-function-development176—~2.9kAutomated safety check: NotesMIT
Acl AbuseADScanPro/Claude-AD211—~2.6kAutomated safety check: PassMIT

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Categories

Questions about Detecting Kerberoasting Attacks

What does Detecting Kerberoasting Attacks do?

Detect Kerberoasting attacks by monitoring for anomalous Kerberos TGS requests (Event ID 4769) targeting service accounts with SPNs, which attackers request offline to crack service account passwords. Detecting Kerberoasting Attacks is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Detect Kerberoasting attacks by monitoring for anomalous Kerberos TGS requests (Event ID 4769) targeting service accounts with SPNs, which attackers request offline to crack service account passwords.

When should I use Detecting Kerberoasting Attacks?

Detecting Kerberoasting Attacks fits situations like: hunting for MITRE T1558 credential access activity; investigating suspected service account password cracking attempts in Active Directory Kerberos logs.

How do I install Detecting Kerberoasting Attacks in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-kerberoasting-attacks -a claude-code`. Or copy the skill folder (skills/detecting-kerberoasting-attacks in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/detecting-kerberoasting-attacks in your project. Claude Code loads it when a task matches its description.

How do I install Detecting Kerberoasting Attacks in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-kerberoasting-attacks -a codex`. Or copy the skill folder (skills/detecting-kerberoasting-attacks in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/detecting-kerberoasting-attacks in your project. Codex loads it when a task matches its description.

Can I use Detecting Kerberoasting Attacks in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-kerberoasting-attacks -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/detecting-kerberoasting-attacks, .gemini/skills/detecting-kerberoasting-attacks, .github/skills/detecting-kerberoasting-attacks and .opencode/skills/detecting-kerberoasting-attacks in your project.

What does Detecting Kerberoasting Attacks need to run?

Going by SKILL.md and its folder, Detecting Kerberoasting Attacks needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Detecting Kerberoasting Attacks access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Detecting Kerberoasting Attacks safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Detecting Kerberoasting Attacks use?

Detecting Kerberoasting Attacks is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Detecting Kerberoasting Attacks use?

About 914 tokens (SKILL.md is roughly 3.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.5k tokens, read only when the agent opens those files.

What are the alternatives to Detecting Kerberoasting Attacks?

Skills that share tags, products or a category with Detecting Kerberoasting Attacks: Authorization Bypass Detection (Tencent/AI-Infra-Guard, 6.8k stars), Run Assert Eval (responsibleai/ASSERT, 330 stars), Osint Methodology (elementalsouls/Claude-OSINT, 2.8k stars) and Lfd Design (elvisun/loss-function-development, 176 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Detecting Kerberoasting Attacks?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 33,993 GitHub stars. The repository holds 644 skills in this directory. The repository was last updated on August 31, 2026.

Source: mukul975/Anthropic-Cybersecurity-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.