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Agent skill

Detecting Deepfake Audio In Vishing Attacks

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Detect AI-generated deepfake audio used in voice phishing (vishing) by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with…

Apache-2.0Auto-check passedMedia & Creative

Install Detecting Deepfake Audio In Vishing Attacks

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-deepfake-audio-in-vishing-attacks -a claude-code

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

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills detecting-deepfake-audio-in-vishing-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-deepfake-audio-in-vishing-attacks .claude/skills/detecting-deepfake-audio-in-vishing-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-deepfake-audio-in-vishing-attacks
GitHub stars
34k
Token cost
~2.8k tokens
SKILL.md length
798 words
Files
4 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Detect AI-generated deepfake audio used in voice phishing (vishing) by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with…

  • Works in 6 steps: Audio Preprocessing → Extract Spectral Features → Build Feature Vector and Classify → …
  • Deepfake voice detection
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 2 more sections
  • Runs Python scripts from its folder

What it does

Detecting Deepfake Audio In Vishing Attacks is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Detect AI-generated deepfake audio used in voice phishing (vishing) by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with machine learning models, supporting batch audio analysis, confidence scoring, and forensic reporting. Use for deepfake voice detection, vishing investigations, AI-generated speech analysis, voice cloning detection, or audio authenticity verification.

Its SKILL.md is about 2.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/api-reference.md` and `scripts/agent.py`).

It sits in Media & Creative, covering Text to speech and voice and Machine learning. 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

  • Deepfake voice detection
  • Vishing investigations
  • AI-generated speech analysis
  • Voice cloning detection

Example prompts

  • “/detecting-deepfake-audio-in-vishing-attacks”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Audio Preprocessing
  2. Extract Spectral Features
  3. Build Feature Vector and Classify
  4. Temporal Artifact Analysis
  5. Spectrogram Visual Inspection
  6. Generate Forensic Report

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 1 file 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 Deepfake Audio In Vishing Attacks loads about 2.8k tokens when it runs, and up to ~4.2k if it reads all its reference files. Until then it costs about 122 tokens; SKILL.md has 798 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~122
When it runs · the whole SKILL.md, loaded when a task matches
~2.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.2k

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). 798 words, ~2,844 tokens.

Download SKILL.mdSave it as .claude/skills/detecting-deepfake-audio-in-vishing-attacks/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
detecting-deepfake-audio-in-vishing-attacks
description
Detect AI-generated deepfake audio used in voice phishing (vishing) by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with machine learning models, supporting batch audio analysis, confidence scoring, and forensic reporting. Use for deepfake voice detection, vishing investigations, AI-generated speech analysis, voice cloning detection, or audio authenticity verification.
domain
cybersecurity
subdomain
social-engineering-defense
tags
deepfake-detection, vishing, audio-forensics, MFCC, spectral-analysis, voice-cloning
version
1.0.0
author
mukul975
license
Apache-2.0
atlas_techniques
AML.T0088, AML.T0043, AML.T0018, AML.T0052
nist_ai_rmf
MEASURE-2.7, GOVERN-6.2, MAP-5.2, MEASURE-2.5, MAP-5.1
d3fend_techniques
Sender Reputation Analysis, Content Validation, Message Analysis, User Behavior Analysis, Identifier Analysis
nist_csf
PR.AT-01, DE.CM-09, RS.CO-02

Detecting Deepfake Audio in Vishing Attacks

When to Use

  • A suspected vishing call used an AI-cloned executive voice to authorize a wire transfer
  • Security operations received a voicemail that sounds like the CEO but the tone seems off
  • Incident response needs to determine whether a recorded phone call contains synthetic speech
  • Fraud investigation requires forensic proof that audio was AI-generated
  • Red team exercises use voice cloning and blue team needs detection capability

Do not use for text-based phishing (email/SMS); use email header analysis or URL detonation tools instead.

Prerequisites

  • Python 3.9+ with librosa, numpy, scikit-learn, and scipy installed
  • Audio samples in WAV, MP3, or FLAC format (mono or stereo, any sample rate)
  • Reference corpus of known genuine voice samples for the targeted individual (optional but improves accuracy)
  • FFmpeg installed for audio format conversion (librosa dependency)
  • Minimum 3 seconds of audio for reliable feature extraction

Workflow

Step 1: Audio Preprocessing

Normalize and prepare audio samples for feature extraction:

python
import librosa
import numpy as np

# Load audio, resample to 16kHz mono
y, sr = librosa.load("suspect_call.wav", sr=16000, mono=True)

# Trim silence from beginning and end
y_trimmed, _ = librosa.effects.trim(y, top_db=25)

# Normalize amplitude to [-1, 1]
y_norm = y_trimmed / np.max(np.abs(y_trimmed))

Audio preprocessing ensures consistent feature extraction across different recording conditions, microphones, and codec artifacts.

Step 2: Extract Spectral Features

Extract the feature set that distinguishes real from synthetic speech:

Mel-Frequency Cepstral Coefficients (MFCCs):

python
# Extract 20 MFCCs + delta and delta-delta
mfccs = librosa.feature.mfcc(y=y_norm, sr=sr, n_mfcc=20)
mfcc_delta = librosa.feature.delta(mfccs)
mfcc_delta2 = librosa.feature.delta(mfccs, order=2)

MFCCs capture the spectral envelope of speech, representing how the vocal tract shapes sound. Deepfake audio often shows unnatural smoothness in higher-order MFCCs because neural vocoders approximate but do not perfectly replicate the acoustic resonance of a physical vocal tract.

Spectral Features:

python
spectral_centroid = librosa.feature.spectral_centroid(y=y_norm, sr=sr)
spectral_bandwidth = librosa.feature.spectral_bandwidth(y=y_norm, sr=sr)
spectral_contrast = librosa.feature.spectral_contrast(y=y_norm, sr=sr)
spectral_rolloff = librosa.feature.spectral_rolloff(y=y_norm, sr=sr)
zero_crossing_rate = librosa.feature.zero_crossing_rate(y_norm)

Key indicators of deepfake audio:

  • Reduced spectral contrast in the 4-8 kHz range (vocoders compress high-frequency detail)
  • Abnormally consistent spectral centroid over time (real speech has natural variation)
  • Lower zero-crossing rate variance (synthetic speech lacks micro-perturbations)
  • Missing or attenuated formant transitions during consonant-vowel boundaries
Step 3: Build Feature Vector and Classify

Aggregate frame-level features into a fixed-length vector and classify:

python
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.model_selection import cross_val_score

def build_feature_vector(y, sr):
    features = []
    mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=20)
    for coeff in mfccs:
        features.extend([np.mean(coeff), np.std(coeff), np.min(coeff), np.max(coeff)])
    for feat_fn in [librosa.feature.spectral_centroid,
                    librosa.feature.spectral_bandwidth,
                    librosa.feature.spectral_rolloff,
                    librosa.feature.zero_crossing_rate]:
        feat = feat_fn(y=y, sr=sr) if feat_fn != librosa.feature.zero_crossing_rate else feat_fn(y)
        features.extend([np.mean(feat), np.std(feat), np.min(feat), np.max(feat)])
    contrast = librosa.feature.spectral_contrast(y=y, sr=sr)
    for band in contrast:
        features.extend([np.mean(band), np.std(band)])
    return np.array(features)

Classification uses an ensemble approach: Random Forest for robustness and Gradient Boosting for accuracy, with a voting mechanism to reduce false positives.

Step 4: Temporal Artifact Analysis

Examine time-domain artifacts that neural vocoders leave behind:

python
# Pitch stability analysis - deepfakes often have unnaturally stable F0
f0, voiced_flag, voiced_probs = librosa.pyin(y_norm, fmin=50, fmax=500, sr=sr)
f0_clean = f0[~np.isnan(f0)]
pitch_std = np.std(f0_clean) if len(f0_clean) > 0 else 0
pitch_jitter = np.mean(np.abs(np.diff(f0_clean))) if len(f0_clean) > 1 else 0

Real human speech exhibits natural pitch jitter (micro-variations in fundamental frequency) and shimmer (amplitude perturbations). Deepfake audio generated by Tacotron 2, VALL-E, or ElevenLabs typically shows reduced jitter and shimmer compared to genuine speech.

Step 5: Spectrogram Visual Inspection

Generate spectrograms for manual forensic review:

python
import librosa.display
import matplotlib.pyplot as plt

fig, axes = plt.subplots(2, 2, figsize=(14, 10))
librosa.display.specshow(librosa.power_to_db(librosa.feature.melspectrogram(y=y_norm, sr=sr)),
                         sr=sr, ax=axes[0, 0], x_axis='time', y_axis='mel')
axes[0, 0].set_title('Mel Spectrogram')
librosa.display.specshow(mfccs, sr=sr, ax=axes[0, 1], x_axis='time')
axes[0, 1].set_title('MFCCs')

Visual inspection reveals banding artifacts in mel spectrograms, unnatural energy cutoffs above the vocoder's frequency ceiling, and periodic noise patterns in the high-frequency range that are characteristic of neural speech synthesis.

Step 6: Generate Forensic Report

Compile findings into an actionable report:

DEEPFAKE AUDIO ANALYSIS REPORT
================================
File:              suspect_executive_call.wav
Duration:          47.3 seconds
Sample Rate:       16000 Hz
Analysis Date:     2026-03-19

CLASSIFICATION RESULT
Verdict:           LIKELY DEEPFAKE (confidence: 94.2%)
Ensemble Score:    RF=0.91, GBT=0.97, Avg=0.94

FEATURE ANOMALIES DETECTED
- MFCC variance in coefficients 13-20: 62% below genuine baseline
- Spectral contrast (4-8 kHz): 0.23 (genuine avg: 0.41)
- Pitch jitter: 0.8 Hz (genuine avg: 2.4 Hz)
- Zero-crossing rate std: 0.003 (genuine avg: 0.011)

SPECTROGRAM ARTIFACTS
- Energy cutoff above 7.8 kHz (consistent with neural vocoder ceiling)
- Banding pattern at 50ms intervals in mel spectrogram
- Missing formant transitions at 12.4s, 23.1s, 35.7s timestamps

RECOMMENDATION
High confidence of AI-generated audio. Recommend out-of-band
verification with the purported speaker. Preserve original audio
file with chain of custody documentation for potential legal action.

Key Concepts

TermDefinition
MFCCMel-Frequency Cepstral Coefficients; representation of the short-term power spectrum on a mel (perceptual) frequency scale
Spectral CentroidWeighted mean of frequencies present in the signal; indicates perceived brightness of a sound
Spectral ContrastDifference in amplitude between peaks and valleys in the spectrum across frequency sub-bands
VocoderSignal processing component that synthesizes audio waveforms from acoustic features; used in TTS and voice cloning
Pitch JitterCycle-to-cycle variation in fundamental frequency; natural in human speech, reduced in synthetic speech
VishingVoice phishing; social engineering attack conducted via phone calls, increasingly using AI-cloned voices
FormantResonant frequencies of the vocal tract that define vowel sounds; transitions between formants are difficult for AI to replicate perfectly
Show full SKILL.md (266 more words)Show less

Tools & Systems

  • librosa: Python library for audio analysis providing MFCC, spectral feature extraction, and spectrogram generation
  • scikit-learn: Machine learning library used for Random Forest and Gradient Boosting classification
  • Resemblyzer: Speaker embedding library for comparing voice identity between known genuine and suspect samples
  • Speechbrain: Deep learning toolkit for speech processing with pretrained deepfake detection models
  • Praat: Phonetics software for detailed pitch, jitter, and shimmer analysis of speech samples
  • FFmpeg: Audio format conversion and preprocessing utility required by librosa

Common Scenarios

Scenario: Executive Impersonation Wire Transfer Fraud

Context: CFO receives a phone call appearing to be from the CEO requesting an urgent wire transfer of $2.3M. The call came from an unknown number but the voice sounded identical to the CEO. IT security was able to obtain a recording of the call from the phone system.

Approach:

  1. Extract the audio from the phone system recording and convert to WAV at 16kHz
  2. Run MFCC and spectral feature extraction on the suspect audio
  3. Compare against known genuine CEO voice samples from recorded meetings
  4. Analyze pitch jitter and shimmer against human speech baselines
  5. Classify using the trained ensemble model and generate confidence score
  6. Produce forensic report with spectrogram evidence for legal/compliance

Pitfalls:

  • Phone codec compression (G.711, AMR) degrades audio quality and can mask deepfake artifacts
  • Short audio clips (under 3 seconds) produce unreliable feature statistics
  • Background noise from the call environment can reduce classification accuracy
  • Highly sophisticated voice cloning (e.g., fine-tuned VALL-E with 30+ minutes of training data) may evade basic feature analysis
  • Genuine speech transmitted through VoIP may exhibit spectral artifacts similar to deepfakes

© 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 3 other files (scripts, references) in skills/detecting-deepfake-audio-in-vishing-attacks of mukul975/Anthropic-Cybersecurity-Skills.

  • SKILL.md
  • LICENSE
  • references/api-reference.md
  • scripts/agent.py

Open the folder on GitHubat commit 54a7988

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Categories

Questions about Detecting Deepfake Audio In Vishing Attacks

What does Detecting Deepfake Audio In Vishing Attacks do?

Detect AI-generated deepfake audio used in voice phishing (vishing) by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with…. Detecting Deepfake Audio In Vishing Attacks is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Detect AI-generated deepfake audio used in voice phishing (vishing) by extracting spectral features (MFCC, spectral centroid, spectral contrast, zero-crossing rate) and classifying samples with machine learning models, supporting batch audio analysis, confidence scoring, and forensic reporting.

When should I use Detecting Deepfake Audio In Vishing Attacks?

Detecting Deepfake Audio In Vishing Attacks fits situations like: deepfake voice detection; vishing investigations; AI-generated speech analysis; voice cloning detection.

How do I install Detecting Deepfake Audio In Vishing Attacks in Claude Code?

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

How do I install Detecting Deepfake Audio In Vishing Attacks in Codex?

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

Can I use Detecting Deepfake Audio In Vishing 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-deepfake-audio-in-vishing-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-deepfake-audio-in-vishing-attacks, .gemini/skills/detecting-deepfake-audio-in-vishing-attacks, .github/skills/detecting-deepfake-audio-in-vishing-attacks and .opencode/skills/detecting-deepfake-audio-in-vishing-attacks in your project.

What does Detecting Deepfake Audio In Vishing Attacks need to run?

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

Does Detecting Deepfake Audio In Vishing 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 Deepfake Audio In Vishing 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 Deepfake Audio In Vishing Attacks use?

Detecting Deepfake Audio In Vishing 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 Deepfake Audio In Vishing Attacks use?

About 2.8k tokens (SKILL.md is roughly 11k 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 1.4k tokens, read only when the agent opens those files.

What are the alternatives to Detecting Deepfake Audio In Vishing Attacks?

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Who maintains Detecting Deepfake Audio In Vishing Attacks?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 34,116 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.