SKILLEMALL.ai

CC attacking-oauth-with-device-code-phishing

Run OAuth 2.0 device-code and illicit-consent phishing attacks against Microsoft Entra ID, using TokenTactics-style tooling to steal access and refresh tokens, bypass MFA, and pivot across Microsoft 365 services. Use for authorized red-team engagements simulating device-code or consent-grant phishing against a tenant you have explicit written permission to test.

mukul975/Anthropic-Cybersecurity-Skills Agent Skills author: mukul975 Apache-2.0 5 files · 1 script body ≈ 3 611 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Run OAuth 2.0 device-code and illicit-consent phishing attacks against Microsoft Entra ID, using TokenTactics-style tooling to steal access and refresh…

As a process C 55/100 · Has gaps — weak spots: result and completion, failures and branches, running it twice

IntegrationGitHubSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
C
89/100
safety, quality, tests
Safety 60%
98
Quality 40%
75
Run on models
none yet
Process rating
C
55/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Secrets in code secret-high-entropy-token SKILL.md:200
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      4. The consented OAuth grant persists as a service-principal grant in the victim tenant until an admin revokes it (`Remo…ant`).
      quoted
    • low Risky intent intent-offensive-security SKILL.md:222
      Offensive-security / dual-use content (legitimate for authorised testing; review intended use)
      | Microsoft / Storm-2372 advisory | Device-code phishing campaign analysis | https://www.microsoft.com/en-us/security/blog/ |

    Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "domain"
    • note frontmatter-key unknown frontmatter key "subdomain"
    • note frontmatter-key unknown frontmatter key "nist_csf"
    • note frontmatter-key unknown frontmatter key "mitre_attack"

    Process rating: all ten parameters 55/100

    • 0Result and completion. Does not say what the result is
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 4 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 45 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3611 tokens

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -42 reference files, but SKILL.md never points to them: the model will not open them
    • -31 of 1 scripts are never mentioned in SKILL.md
    • +2Single-language instructions
    • +3Description length 364: enough signal without eating the budget
    • +4Structure: 16 headings
    • +3Step-by-step instructions: 45 items
    • +4Has examples (12 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.