Top AI Attack Vectors You Should Know
Shak
This is a quickly evolving threat landscape and requires continuous monitoring as things change especially as we see shadow AI creep into many areas.
How to read the ranking
Prevalence signal = the strongest public metric we could find (incident share, growth %, case counts or monetary loss).
Lower-ranked vectors are either new but growing (agentic attacks, poisoning) or still mostly academic (model inversion, T-shirt patches) worth monitoring but not yet as common in real IR caseloads.
| Rank | Vector | Why it tops the chart (prevalence signal) |
| 1 | Generative social-engineering (deep-fake BEC, vishing, “synthetic insiders”) | Still the #1 initial-access route: 36 % of Unit 42 IR cases began with social-engineering between May ʼ24-May ʼ25 |
| 2 | Malware posing as AI tools | 115 % YoY jump in malicious files that mimic ChatGPT (177 unique binaries in Jan-Apr ʼ25) |
| 3 | Prompt-injection / LLM hijacking | Microsoft calls indirect prompt injection “one of the most widely-used AI vuln classes” and the top issue in OWASP LLM Top 10 2025 |
| 4 | AI-generated homograph & typosquat domains | Unit 42 shows automated Unicode look-alike domain generation accelerating phishing throughput (July ʼ25 report) |
| 5 | Malicious AI-coding-tool extensions & IDE supply-chain abuse | Dev environments are an attractive, low-friction target; first major campaign netted $500 k (Cursor, Jul ʼ25) |
| 6 | QR-/image- based multi-modal injection (“quishing”) | 784 UK cases, £3.5 M lost to rogue parking-lot QR codes Apr ʼ24-Apr ʼ25; 26 % of phishing links now QR-borne |
| 7 | “Vibe-coding” vulnerabilities (AI-written code by non-devs) | 45 % of GenAI-written snippets introduce OWASP-Top-10 flaws (Veracode GenAI Code Security Report, Aug ʼ25) |
| 8 | Employment-fraud / synthetic-insider infiltration | CrowdStrike tallied 320 hires secured with AI-generated identities in 12 months (funding DPRK missile program) |
| 9 | Autonomous attack agents & AI-assisted malware | CMU & Anthropic reproduced the Equifax 2017 breach end-to-end with an LLM planning its own kill-chain (Jul ʼ25) |
| 10 | Model / dataset poisoning (“sleeper agents”) | “Sleeper Agents” study proved back-doors survive standard RLHF; widely cited (> 200 citations) |
| 11 | Model inversion & membership inference | Black-box attack on Llama-3.2 reconstructed PII from a medical fine-tune (Jul ʼ25) |
| 12 | Adversarial patches / physical evasion | Diffusion-generated T-shirt patches keep wearers invisible to CCTV YOLO-v5 with ~65 % success (May ʼ25 AdvReal paper) |
Key takeaways
MITRE ATLAS is the de-facto source for real attack technique mapping; treat it like ATT&CK for anything model-centric.
OWASP lists give you the quickest “what can go wrong” checklist for both classic ML pipelines and chat-GPT-style apps.
NIST RMF + ISO 42001 provide governance scaffolding that partners and auditors increasingly expect.
SAIF & CSA AICM convert high-level risk goals into concrete, cloud-ready controls.
The EU AI Act is now the enforcement tail-wind—if you sell or deploy AI in Europe, map obligations early.
Use these frameworks together: start threat modelling with OWASP & ATLAS, build controls with SAIF/AICM, and prove compliance with NIST RMF, ISO 42001 and the AI Act.
Subscribe to my newsletter
Read articles from Shak directly inside your inbox. Subscribe to the newsletter, and don't miss out.
Written by