The authors evaluated an Autonomous Adversary system in two enterprise-like lateral-movement scenarios.
Cyber agents hide critical weaknesses behind successful attack workflows
Across enterprise-like lateral-movement scenarios, diagnostic evaluation exposed costly retries, weak recovery, and overly optimistic validation that success rates missed.
Industry
Saeedeh Lohrasbi · Mohammad Mamun · Ahmed Yehia · Scott Buffett · Sherif Saad
National Research Council · University of Windsor
Research Digest··2 min read
Lohrasbi et al.
Why this paper
From National Research Council and University of Windsor · Released code
In one line
Multi-stage LLM cyber agents face persistent bottlenecks in credential and lateral-movement tasks that success rates alone do not reveal.
What it released
Code
What we could check
- ✓Code link in the paper (github.com)
- ·No weights link found
- ·No dataset link found
- ·No compute details found
- ✓Limitations stated by the authors (3 noted)
- ·No benchmark numbers found
Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.
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