What are the most common AI/LLM security threats?

The most common AI/LLM security threats include prompt injection (direct and indirect), data poisoning, model theft and extraction, supply chain attacks, denial of service against model endpoints, insecure output handling, and sensitive information disclosure through LLM responses.

What is prompt injection and how does it work?

Prompt injection is an attack where malicious input is crafted to override or bypass an AI system's safety instructions. Direct prompt injection targets the system prompt, while indirect prompt injection embeds instructions in external content like web pages or documents that the AI processes.

What is model theft and data poisoning in AI systems?

Model theft involves extracting an AI model's parameters, architecture, or training data through API queries or side-channel attacks. Data poisoning occurs when an attacker injects malicious data into the training set, causing the model to learn incorrect behaviors or backdoors that can be triggered later.

How can organizations mitigate AI supply chain attacks?

Organizations can mitigate AI supply chain attacks by vetting model sources and verifying checksums, scanning models for backdoors using tools like ModelScan, implementing strict dependency pinning, monitoring for typosquatting packages, and maintaining a software bill of materials (SBOM) for AI components.

Ancaman Sistem AI

Katalog komprehensif tentang kerentanan khusus AI dan vektor serangan

Ancaman Kritis

Immediate risks with potential for severe impact. Require urgent remediation.

Risiko Tinggi

Serious vulnerabilities that should be addressed promptly to reduce exposure.

Strategi Pertahanan

Praktik dan mitigasi terbaik untuk mengurangi paparan ancaman AI.

Kategori Ancaman

Prompt Injection

Critical

Crafted inputs designed to manipulate model behavior, override safeguards, or extract sensitive information.

Pendekatan Pengujian

Craft adversarial prompts with hidden instructions or special characters
Attempt multi-turn injection chaining
Test for jailbreak bypass of alignment filters
Evaluate output sanitization and safety layers

Training Data Poisoning

Critical

Malicious or biased data introduced into training pipelines, compromising model integrity and reliability.

Pendekatan Pengujian

Analyze data provenance and supply chain
Inject poisoned samples and assess downstream effects
Test resilience to mislabeled or manipulated data
Review validation and anomaly detection mechanisms

Model Inversion

High

Reconstructing training data or sensitive attributes from model outputs, leading to privacy breaches.

Pendekatan Pengujian

Attempt to recover representative training samples
Test susceptibility to membership inference attacks
Evaluate differential privacy protections
Assess risk of leaking PII from embeddings

Adversarial Examples

High

Inputs intentionally perturbed to cause misclassification, hallucinations, or other erroneous outputs.

Pendekatan Pengujian

Generate gradient-based adversarial examples
Apply noise and perturbation attacks
Check model consistency across variations
Evaluate robustness against transfer attacks

Model Stealing

High

Extraction of model functionality or parameters through repeated queries or side-channel analysis.

Pendekatan Pengujian

Simulate query-based model extraction
Analyze API rate limits and response variability
Check for fingerprinting vulnerabilities
Test throttling and monitoring protections

Data Memorization Leakage

High

Sensitive information unintentionally memorized by AI models, retrievable via crafted prompts.

Pendekatan Pengujian

Probe for known secret patterns in outputs
Test for repeated exposure of sensitive training data
Assess risk of accidental PII disclosure

Model Misuse & Malicious Automation

High

AI leveraged to perform tasks outside intended scope, enabling social engineering, spam, or automated attacks.

Pendekatan Pengujian

Simulate misuse scenarios using sandbox models
Test AI output moderation and guardrails
Assess monitoring alerts for abnormal behaviors

Praktik Terbaik Pengujian & Pertahanan

Lingkungan Pengujian yang Aman

Gunakan contoh kotak pasir atau replika untuk pengujian
Jangan pernah melakukan pengujian tanpa izin pada sistem produksi
Menerapkan kemampuan pemantauan, logging, dan rollback

Dokumentasi & Observabilitas

Pertahankan log pengujian dan bukti secara mendetail
Tangkap respons model agar dapat direproduksi
Tandai, klasifikasikan, dan atur kasus uji untuk audit di masa mendatang

Kepatuhan Hukum & Etika

Tetap dalam cakupan dan kontrak resmi
Hormati perlindungan data, undang-undang privasi, dan kekayaan intelektual
Ikuti pengungkapan yang bertanggung jawab dan pengungkapan kerentanan terkoordinasi

Pemantauan & Mitigasi

Terapkan deteksi anomali untuk keluaran AI yang tidak biasa
Tinjau batas tarif, akses API, dan pola kueri secara berkala
Integrasikan peringatan real-time untuk ancaman kritis

Referensi & Sumber Daya

AI Hacking Team

The AI Hacking team researches and documents AI/LLM security vulnerabilities, red teaming techniques, and defensive strategies. Our guides are based on real-world pentesting experience and continuous monitoring of the AI security landscape.

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Ancaman Sistem AI

Ancaman Kritis

Risiko Tinggi

Strategi Pertahanan

Kategori Ancaman

Prompt Injection

Training Data Poisoning

Model Inversion

Adversarial Examples

Model Stealing

Data Memorization Leakage

Model Misuse & Malicious Automation

Praktik Terbaik Pengujian & Pertahanan

Lingkungan Pengujian yang Aman

Dokumentasi & Observabilitas

Kepatuhan Hukum & Etika

Pemantauan & Mitigasi

Referensi & Sumber Daya

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