COMMON WEAKNESS ENUMERATIONIncomplete

CWE-1039

Inadequate Detection or Handling of Adversarial Input Perturbations in Automated Recognition Mechanism

The product uses an automated mechanism such as machine learning to recognize complex data inputs (e.g. image or audio) as a particular concept or category, but it does not properly detect or handle inputs that have been modified or constructed in a way that causes the mechanism to detect a different, incorrect concept.

Soyutlama

Class

Yapı

Simple

Sömürü İhtimali

Medium

Problemi anla

Problem nedir?

The product uses an automated mechanism such as machine learning to recognize complex data inputs (e.g. image or audio) as a particular concept or category, but it does not properly detect or handle inputs that have been modified or constructed in a way that causes the mechanism to detect a different, incorrect concept.

Genişletilmiş açıklama

When techniques such as machine learning are used to automatically classify input streams, and those classifications are used for security-critical decisions, then any mistake in classification can introduce a vulnerability that allows attackers to cause the product to make the wrong security decision or disrupt service of the automated mechanism. If the mechanism is not developed or "trained" with enough input data or has not adequately undergone test and evaluation, then attackers may be able to craft malicious inputs that intentionally trigger the incorrect classification. Targeted technologies include, but are not necessarily limited to: - automated speech recognition - automated image recognition - automated cyber defense - Chatbot, LLMs, generative AI For example, an attacker might modify road signs or road surface markings to trick autonomous vehicles into misreading the sign/marking and performing a dangerous action. Another example includes an attacker that crafts highly specific and complex prompts to "jailbreak" a chatbot to bypass safety or privacy mechanisms, better known as prompt injection attacks.

Kök neden

Problem nasıl ve ne zaman oluşur?

Yaşam döngüsü evresi

Architecture and Design

This issue can be introduced into the automated algorithm itself due to inadequate training data used as well as lack of validation, verification, testing, and evaluation of the algorithm. These factors can affect the overall robustness of the algorithm when introduced into operational settings.

Yaşam döngüsü evresi

Implementation

The developer might not apply external validation of inputs into the algorithm.

Risk

Başarılı sömürü neye yol açar?

Bypass Protection Mechanism

Etkilenen güvenlik alanları: Integrity

When the automated recognition is used in a protection mechanism, an attacker may be able to craft inputs that are misinterpreted in a way that grants excess privileges.
DoS: Resource Consumption (Other)DoS: Instability

Etkilenen güvenlik alanları: Availability

There could be disruption to the service of the automated recognition system, which could cause further downstream failures of the software.
Read Application Data

Etkilenen güvenlik alanları: Confidentiality

This weakness could lead to breaches of data privacy through exposing features of the training data, e.g., by using membership inference attacks or prompt injection attacks.
Varies by Context

Etkilenen güvenlik alanları: Other

The consequences depend on how the application applies or integrates the affected algorithm.

Savunma

Nasıl önlenir ve çözülür?

Architecture and Design

Mimari ve tasarım yaklaşımı

Detay
Algorithmic modifications such as model pruning or compression can help mitigate this weakness. Model pruning ensures that only weights that are most relevant to the task are used in the inference of incoming data and has shown resilience to adversarial perturbed data.

Architecture and Design

Mimari ve tasarım yaklaşımı

Detay
Consider implementing adversarial training, a method that introduces adversarial examples into the training data to promote robustness of algorithm at inference time.

Architecture and Design

Mimari ve tasarım yaklaşımı

Detay
Consider implementing model hardening to fortify the internal structure of the algorithm, including techniques such as regularization and optimization to desensitize algorithms to minor input perturbations and/or changes.

Implementation

Uygulama aşaması savunması

Detay
Consider implementing multiple models or using model ensembling techniques to improve robustness of individual model weaknesses against adversarial input perturbations.

Implementation

Uygulama aşaması savunması

Detay
Incorporate uncertainty estimations into the algorithm that trigger human intervention or secondary/fallback software when reached. This could be when inference predictions and confidence scores are abnormally high/low comparative to expected model performance.

Integration

Integration aşaması savunması

Detay
Reactive defenses such as input sanitization, defensive distillation, and input transformations can all be implemented before input data reaches the algorithm for inference.

Integration

Integration aşaması savunması

Detay
Consider reducing the output granularity of the inference/prediction such that attackers cannot gain additional information due to leakage in order to craft adversarially perturbed data.

Doğrulama

Nasıl tespit edilir?

Dynamic Analysis with Manual Results Interpretation

Use indicators from model performance deviations such as sudden drops in accuracy or unexpected outputs to verify the model.

Dynamic Analysis with Manual Results Interpretation

Use indicators from input data collection mechanisms to verify that inputs are statistically within the distribution of the training and test data.

Architecture or Design Review

Use multiple models or model ensembling techniques to check for consistency of predictions/inferences.