CWE-1039Inadequate Detection or Handling of Adversarial Input Perturbations in Automated Recognition Mechanism

Description

Common consequences· 4

• Integrity — Bypass Protection Mechanism
  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.
• Availability — DoS: Resource Consumption (Other), DoS: Instability
  There could be disruption to the service of the automated recognition system, which could cause further downstream failures of the software.
• Confidentiality — Read Application Data
  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.
• Other — Varies by Context
  The consequences depend on how the application applies or integrates the affected algorithm.

Potential mitigations· 5

• [Architecture and Design]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]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]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]Consider implementing multiple models or using model ensembling techniques to improve robustness of individual model weaknesses against adversarial input perturbations.
• [Implementation]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.

Related by meaning· 6

Nearest entities by semantic similarity across the cs-graph corpus.