CuraSec

tag: Adversarial-Ml · 4 items

Learn archived Adversarial Attacks Can Silently Manipulate VLM Confidence Scores
2026-08-10 · arXiv cs.CR · source ↗ #ai-security#adversarial-ml#vision-language-models
  • Engineer — Learn: White-box attacks can degrade confidence readouts in vision-language models to near-random while leaving the generated answer unchanged, undermining confidence-gated pipelines; teams deploying VLMs with confidence thresholds for access control or oversight should treat confidence as an untrusted signal in adversarial contexts.
  • SOC/IR — Skip
  • Leader — Learn: Academic research showing that AI confidence gating — a common oversight mechanism in deployed vision-language products — can be silently subverted; worth tracking as AI governance frameworks and internal AI-use policies mature, but no immediate action warranted.
Learn archived GUI Agent Guardrails Erode Under Multi-Turn User Persuasion
2026-08-03 · arXiv cs.CR · source ↗ #gui-agents#llm-security#adversarial-ml
  • Engineer — Learn: Research shows that single-turn ASR benchmarks overstate real-world robustness of GUI agent guardrails, with 4-turn escalation chains recovering ~20 points of attack success across all tested models. Teams building or deploying GUI agents should treat static prompt-level alignment as insufficient and evaluate multi-turn threat scenarios in their safety testing.
  • SOC/IR — Skip
  • Leader — Learn: If your organization is piloting or deploying AI GUI agents, this research illustrates that current safety guardrails are weaker than benchmark numbers suggest under realistic multi-turn user interaction — useful context for AI deployment policies and vendor capability reviews, but no immediate action required.
Learn archived ADSD Attack Collapses Speculative Decoding Acceptance, Slows LLM Inference 62%
2026-07-27 · arXiv cs.CR · source ↗ #adversarial-ml#llm-security#speculative-decoding
  • Engineer — Learn: Novel prompt-suffix attack degrades speculative decoding throughput without corrupting outputs, affecting any deployment using draft-target inference acceleration (vLLM, TGI, etc.). No patch or mitigation exists yet; file this when designing LLM serving infrastructure to justify input validation and rate controls at the prompt layer.
  • SOC/IR — Skip
  • Leader — Skip
Learn archived Lucid: Black-box adversarial attacks on multimodal AI agent memory
2026-07-20 · arXiv cs.CR · source ↗ #ai-agents#adversarial-ml#memory-poisoning
  • Engineer — Learn: Research demonstrates that multimodal agent memory pipelines can be poisoned or injected via imperceptible image perturbations with ~60% success rates; no patch exists yet, but teams building RAG or memory-backed AI agents should design for untrusted visual input and avoid unconditional trust in retrieved visual context.
  • SOC/IR — Learn: Novel attack class against AI agent memory systems; no IOCs or exploited-in-the-wild evidence, but detection engineers supporting AI-enabled products should be aware this failure mode exists for future coverage planning.
  • Leader — Skip