Opportunistic Target Selection: Early Directional Commitment for Query-Efficient Black-Box Adversarial Attacks
arXiv:2605.25663v1 Announce Type: new Abstract: Black-box adversarial attacks that minimize only the ground-truth confidence suffer from class drift: perturbations wander through the feature space without committing to a specific adversarial class, wasting queries on diffuse, undirected progress. We introduce Opportunistic Target Selection (OTS), a lightweight wrapper that switches an untargeted attack to a targeted objective early in its trajectory, locking onto whichever non-true class currently leads. OTS requires no architectural modification to the underlying attack, no gradient access, a
The paper addresses a known inefficiency in black-box adversarial attacks, which is an increasingly critical area as AI models are deployed in sensitive applications.
This research significantly improves the efficiency of black-box adversarial attacks, making it easier and faster to find vulnerabilities in AI systems without direct access to their internal workings. This advancement poses a more robust threat to AI security and model integrity.
Adversarial attacks on black-box AI models can now be executed more quickly and with fewer queries, reducing the cost and complexity of finding model weaknesses.
- · Adversarial attack researchers
- · Red teams
- · Cybersecurity researchers
- · AI model developers
- · Organizations deploying black-box AI models without robust defenses
- · AI-reliant systems
Existing black-box AI models become more susceptible to efficient adversarial manipulation.
Increased pressure on AI developers to integrate more sophisticated adversarial robustness techniques into their models.
A potential arms race between more efficient attack methods and more resilient defense mechanisms in AI security.
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Read at arXiv cs.LG