DragonJAR - Hacking y Seguridad Informática·1 hr 10 min
Chema Alonso
Demonstrates genAI por Hacking & Fun and evaluates key implications for security practitioners.
genaivulnerability-researchmachine-learning+2
adversarial-ml
17 talks tagged with this topic.
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IEEE Symposium on Security and Privacy·16 min
Engineers explore architecture and security mechanisms for Intriguing Properties of Adversarial ML Attacks in the Problem Space.
spaceadversarial-mlintriguing+3
The Linux Foundation·39 min
Alan Ho
Analyzes A New Stack for the Gen AI Era and evaluates key implications for security practitioners.
stackvulnerability-researchmachine-learning+2
The Linux Foundation·18 min
Karen Bennet
Discusses AI Profile and evaluates key implications for security practitioners.
profilevulnerability-researchmachine-learning+2
The Linux Foundation·1 hr 21 min
Ibrahim Haddad · Jim Spohrer · Saishruthi Swaminathan · Tao · Prasanth Pulavarthi · Bruce · Julien Le Dem
Analyzes LF AI Mini-Summit and evaluates key implications for security practitioners.
mini-summitvulnerability-researchmachine-learning+2
The Linux Foundation·38 min
Paul Holland · Hewlett Packard Enterprise
Discusses the O[S]PO Organization for the Age of AI and evaluates key implications for security practitioners.
organizationvulnerability-researchmachine-learning+2
App Sec Village·18 min
Wang Zhilong · Xinzhi Luo
Analysis of when and why large language models hallucinate during automated code review, and how misleading identifier names induce errors.
llmaicode-review+1
Main Stage·38 min
Harriet Farlow
Adversarial machine learning techniques used to bypass facial recognition, surveillance, and gameplay monitoring at Canberra Casino.
aifacial-recognitionsurveillance+2
RSAC Cybersecurity·48 min
Industry experts analyze emerging threats surrounding Blackbox Interpretability: Next Frontier in Adversarial ML Evasion.
evasionadversarial-mlblackbox+2
Blacks in Cyber Village·59 min
Birhanu Eshete
Dr. Birhanu Eshete evaluates the trustworthiness of machine learning models in healthcare, autonomous driving, and finance, analyzing adversarial attacks and fairness pitfalls.
machine-learningai-securityadversarial-ml+2
AI Village·1 hr 30 min
Workshop
Hands-on workshop demonstrating adversarial evasion techniques targeting ML-based Antimalware Scan Interface providers.
amsiadversarial-mlevasion+2
AI Village·30 min
Erick Galinkin
A primer breaking down core vocabulary, concepts, and foundational theory in machine learning security.
machine-learningvocabularyadversarial-ml+1
AI Village·22 min
Hyrum Anderson · Zoltan Balaz
Reviews results and top evasion strategies from the AI Village machine learning security and evasion competition.
machine-learningadversarial-mlcontest+1
AI Village·58 min
Journal Club Live
Discusses practical methodologies for discovering exploitable bugs, implementation flaws, and attack surfaces in ML systems.
adversarial-mlvulnerability-researchmachine-learning+1
Main Stage·29 min
Ariel Herbert-Voss
Provides a structured adversarial ML red team methodology for attacking production AI systems beyond naive input perturbation.
ai-securitymachine-learningadversarial-ml+2
AI Village·32 min
Shankar and Kumar
Adversarial machine learning techniques and defense mechanisms.
machine-learningadversarial-mlmodel-attacks+1
Main Track 1·19 min
Hyrum Anderson
AI agent using reinforcement learning to modify malware and evade ML-based antivirus detection.
machine-learningadversarial-mlmalware-evasion+2