mirror of
https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git
synced 2026-06-26 11:44:37 +03:00
mukul975
8cae0648ec
Demand-driven expansion targeting the fastest-growing 2025-2026 threat and
skills categories (ISC2/WEF/CrowdStrike/Mandiant signals):
- AI Security (NEW domain, 12 skills): LLM red-teaming with garak/PyRIT,
prompt injection (direct/indirect/RAG), MCP tool-poisoning, agentic tool
invocation, guardrails, model/data poisoning, system-prompt leakage,
embedding/vector weaknesses, model extraction, continuous red-teaming
- Supply Chain Security (NEW domain, 5 skills): SBOMs, dependency confusion,
malicious-npm triage, typosquatting, SLSA/Sigstore provenance
- Hardware & Firmware Security (NEW domain, 4 skills): CHIPSEC/UEFI audit,
Secure Boot bypass, TPM measured-boot attestation, ESP bootkit hunting
- Identity (10): Entra ID/ROADtools, GraphRunner, AADInternals, ADCS/Certipy,
shadow credentials, coercion, BloodHound CE, device-code phishing, SSO abuse
- Cloud-native (8): Stratus, Pacu, CloudFox, container escape, K8s RBAC,
Falco, Trivy, kube-bench
- Offensive C2 (6): Sliver, Havoc, NetExec, DPAPI, NTLM relay ESC8, redirectors
- DFIR (6): Hayabusa, Chainsaw, KAPE, Velociraptor, EZ Tools, Plaso
- Backfill (4): OpenCTI, MISP, honeytokens, post-quantum crypto migration
Each skill follows the repo taxonomy (SKILL.md + references/{standards,api-reference}.md
+ scripts/agent.py + LICENSE), with researched real tool commands (no placeholders),
complete frontmatter, and ATT&CK/ATLAS + NIST CSF mappings. Updates README domain
table, skill count, and index.json.
2.0 KiB
2.0 KiB
API Reference — Indirect Prompt Injection Detection
LLM Guard
Install: pip install llm-guard
| API | Description |
|---|---|
from llm_guard.input_scanners import PromptInjection |
Import the injection scanner |
PromptInjection(threshold=0.5, match_type=MatchType.FULL) |
Construct scanner (FULL or SENTENCE match) |
scanner.scan(text) |
Returns (sanitized_text, is_valid, risk_score) |
from llm_guard import scan_prompt |
Run multiple scanners over a prompt |
is_valid == False indicates an injection was detected.
Transformers detector models
Install: pip install transformers torch
| API | Description |
|---|---|
pipeline("text-classification", model=...) |
Load a classifier pipeline |
protectai/deberta-v3-base-prompt-injection-v2 |
Open prompt-injection classifier (labels: SAFE / INJECTION) |
meta-llama/Llama-Prompt-Guard-2-86M |
Meta jailbreak/injection classifier (gated license) |
Content extraction
| API | Description |
|---|---|
BeautifulSoup(html, "html.parser") |
Parse HTML |
soup.find_all(string=lambda t: isinstance(t, Comment)) |
Extract HTML comments |
pypdf.PdfReader(path).pages[i].extract_text() |
Extract PDF text |
pytesseract.image_to_string(Image.open(path)) |
OCR text from an image |
PIL.Image.open(path)._getexif() |
Read EXIF metadata |
Normalization helpers
| Technique | Method |
|---|---|
| Strip zero-width chars | str.translate over U+200B..U+FEFF |
| Strip Unicode tag chars | filter ord in range 0xE0000-0xE007F |
| Canonicalize | unicodedata.normalize("NFKC", text) |
| Decode Base64 | base64.b64decode(token) |
| Decode ROT13 | codecs.decode(text, "rot_13") |
External References
- LLM Guard PromptInjection docs: https://llm-guard.com/input_scanners/prompt_injection/
- Hugging Face transformers pipelines: https://huggingface.co/docs/transformers/main_classes/pipelines
- pytesseract: https://github.com/madmaze/pytesseract