Adversarial Attacks
The short answer · 25 words
In short
Adversarial Attacks are targeted attacks on ML systems in which input or other components are manipulated so that the model produces unwanted or incorrect results.
Flow-alerta.cab (2026): Adversarial Attacks. https://flow-alerta.cab/en/glossar/adversarial-attacks. Editorially reviewed on: 09 October 2026.
How much detail would you like?
Practical
Important for AI security, especially when models make safety-critical decisions or accept input from outside. Robustness must be tested against realistic threat models.
Possible example: An image classifier is tested with specifically optimized pixel changes in order to check whether small manipulations trigger an incorrect classification with high certainty.
Possible applications: security testing of image, document, fraud or classification systems; red teaming before productive use; definition of realistic attacker capabilities and monitoring of eye-catching input.
At a glance
- Core
- A classic case is adversarial examples: inputs are altered slightly in a targeted manner, so that a model misclassifies them. Depending on the taxonomy, the generic term adversarial attacks covers further forms of attack and should not be reduced to image disturbances.
- Use
- Important for AI security, especially when models make safety-critical decisions or accept input from outside. Robustness must be tested against realistic threat models.
- Example
- Possible example: An image classifier is tested with specifically optimized pixel changes in order to check whether small manipulations trigger an incorrect classification with high certainty.
- Limits
- Test only known standard attacks; use unrealistic threat models; interpret robustness on an attack as general security; confuse normal data quality with intentional attacks; evaluate countermeasures without independent evaluation.
In context
Questions about this term
Are adversarial attacks normal model errors?
No, they involve targeted manipulation within the framework of an attack model.
Are small pixel changes the only form?
No, attacks can look different depending on the system and threat model.
All details and background
A classic case is adversarial examples: inputs are altered slightly in a targeted manner, so that a model misclassifies them. Depending on the taxonomy, the generic term adversarial attacks covers further forms of attack and should not be reduced to image disturbances.
ELI5 – Explained simply
Like an intentionally modified test piece that looks almost the same to humans, but hits exactly the vulnerability of the automatic tester.
Why it matters in practice
Important for AI security, especially when models make safety-critical decisions or accept input from outside. Robustness must be tested against realistic threat models.
Example
Possible example: An image classifier is tested with specifically optimized pixel changes in order to check whether small manipulations trigger an incorrect classification with high certainty.
Pitfalls and limits
- Test only known standard attacks; use unrealistic threat models; interpret robustness on an attack as general security; confuse normal data quality with intentional attacks; evaluate countermeasures without independent evaluation.
Origin & history
Szegedy et al. published their preprint in December 2013; Goodfellow et al. described FGSM in December 2014 (ICLR2015).
Use in mid-sized companies
- Possible applications: security testing of image, document, fraud or classification systems; red teaming before productive use; definition of realistic attacker capabilities and monitoring of eye-catching input.
Related terms
- Adversarial Robustness
- Red Teaming
- AI security (safety)
Matching Flow-alerta service
AI opportunity analysis & roadmapAI consulting: AI potential analysis & roadmap on tasks, data and prerequisites of a possible first AI use case.Matching tool / use case
View use casesUse Case: Robustness test of a classifier before rollout; Tools: Adversarial Robustness Toolbox, Foolbox, own test suitesSources
- arxiv.orgSzegedy et al. (2013), Intriguing properties of neural networks
- arxiv.orgGoodfellow, Shlens & Szegedy (2014), Explaining and Harnessing Adversarial Examples
Quellenprüfung: 09.10.2026. Anwendungsbeispiele sind mögliche Szenarien, keine behaupteten Kundenfälle.
Editorially reviewed on: