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.

एआई सिस्टम खतरे

एआई-विशिष्ट कमजोरियों और हमले वैक्टर की व्यापक सूची

गंभीर खतरे

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

उच्च जोखिम

Serious vulnerabilities that should be addressed promptly to reduce exposure.

रक्षा रणनीतियाँ

एआई खतरे के जोखिम को कम करने के लिए सर्वोत्तम अभ्यास और शमन।

खतरा श्रेणियां

Prompt Injection

Critical

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

परीक्षण दृष्टिकोण

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.

परीक्षण दृष्टिकोण

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.

परीक्षण दृष्टिकोण

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.

परीक्षण दृष्टिकोण

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.

परीक्षण दृष्टिकोण

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.

परीक्षण दृष्टिकोण

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.

परीक्षण दृष्टिकोण

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

परीक्षण और रक्षा सर्वोत्तम अभ्यास

सुरक्षित परीक्षण वातावरण

परीक्षण के लिए सैंडबॉक्स या प्रतिकृति उदाहरणों का उपयोग करें
उत्पादन प्रणालियों पर कभी भी अनधिकृत परीक्षण न करें
निगरानी, लॉगिंग और रोलबैक क्षमताओं को लागू करें

दस्तावेज़ीकरण और अवलोकन

विस्तृत परीक्षण लॉग और साक्ष्य बनाए रखें
प्रतिलिपि प्रस्तुत करने योग्यता के लिए मॉडल प्रतिक्रियाओं को कैप्चर करें
भविष्य के ऑडिट के लिए परीक्षण मामलों को टैग, वर्गीकृत और व्यवस्थित करें

कानूनी और नैतिक अनुपालन

अधिकृत दायरे और अनुबंधों के भीतर रहें
डेटा सुरक्षा, गोपनीयता कानूनों और बौद्धिक संपदा का सम्मान करें
जिम्मेदार प्रकटीकरण और समन्वित भेद्यता प्रकटीकरण का पालन करें

निगरानी और शमन

असामान्य एआई आउटपुट के लिए विसंगति का पता लगाना
नियमित रूप से दर सीमा, एपीआई पहुंच और क्वेरी पैटर्न की समीक्षा करें
के लिए वास्तविक समय अलर्ट को एकीकृत करें गंभीर धमकियाँ

संदर्भ और संसाधन

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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एआई सिस्टम खतरे

गंभीर खतरे

उच्च जोखिम

रक्षा रणनीतियाँ

खतरा श्रेणियां

Prompt Injection

Training Data Poisoning

Model Inversion

Adversarial Examples

Model Stealing

Data Memorization Leakage

Model Misuse & Malicious Automation

परीक्षण और रक्षा सर्वोत्तम अभ्यास

सुरक्षित परीक्षण वातावरण

दस्तावेज़ीकरण और अवलोकन

कानूनी और नैतिक अनुपालन

निगरानी और शमन

संदर्भ और संसाधन

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