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.

AI-systemtrusler

Omfattende katalog over AI-spesifikke vektorsårbarheter og angrepsvektorsårbarheter

Kritiske trusler

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

Høy risiko

Serious vulnerabilities that should be addressed promptly to reduce exposure.

Forsvarsstrategier

Beste fremgangsmåter og begrensninger for å redusere eksponering av AI-trusler.

Trusselkategorier

Prompt Injection

Critical

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

Testtilnærming

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.

Testtilnærming

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.

Testtilnærming

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.

Testtilnærming

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.

Testtilnærming

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.

Testtilnærming

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.

Testtilnærming

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

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Sikkert testmiljø

Bruk sandkasse- eller replika-forekomster, Un-planer for tradisjonelle LAI|-testing| delegere og handle på tvers av verktøy og systemer. Dette skaper nye angrepsflater som krever spesialiserte sikkerhetstilnærminger.
Utfør aldri uautoriserte tester på produksjonssystemer
Implementer overvåkings-, logg- og tilbakeføringsfunksjoner

Dokumentasjon og observerbarhet

Oppretthold detaljerte testlogger og bevis
Registrer modellsvar for reproduserbarhet
Relaterte ressurser

Juridisk og etisk overholdelse

Hold deg innenfor autorisert omfang og kontrakter
Respekter databeskyttelse, personvernlover og immaterielle rettigheter
Følg ansvarlig avsløring og koordinert avsløring av sårbarheter

Overvåking og avbøtende

Implementer anomalideteksjon for uvanlige AI-utganger
Gjennomgå hastighetsgrenser, API-tilgang og spørringsmønstre
Integrate real-time alerting for critical threats

Referanser og ressurser

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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AI-systemtrusler

Kritiske trusler

Høy risiko

Forsvarsstrategier

Trusselkategorier

Prompt Injection

Training Data Poisoning

Model Inversion

Adversarial Examples

Model Stealing

Data Memorization Leakage

Model Misuse & Malicious Automation

se beste praksis-forsvar

Sikkert testmiljø

Dokumentasjon og observerbarhet

Juridisk og etisk overholdelse

Overvåking og avbøtende

Referanser og ressurser

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