Pith. sign in

REVIEW 5 cited by

CAI: An Open, Bug Bounty-Ready Cybersecurity AI

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2504.06017 v2 pith:WGJZSH6B submitted 2025-04-08 cs.CR

CAI: An Open, Bug Bounty-Ready Cybersecurity AI

classification cs.CR
keywords cybersecuritysecuritybountytestingbenchmarksfasterfirstframework
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

By 2028 most cybersecurity actions will be autonomous, with humans teleoperating. We present the first classification of autonomy levels in cybersecurity and introduce Cybersecurity AI (CAI), an open-source framework that democratizes advanced security testing through specialized AI agents. Through rigorous empirical evaluation, we demonstrate that CAI consistently outperforms state-of-the-art results in CTF benchmarks, solving challenges across diverse categories with significantly greater efficiency -up to 3,600x faster than humans in specific tasks and averaging 11x faster overall. CAI achieved first place among AI teams and secured a top-20 position worldwide in the "AI vs Human" CTF live Challenge, earning a monetary reward of $750. Based on our results, we argue against LLM-vendor claims about limited security capabilities. Beyond cybersecurity competitions, CAI demonstrates real-world effectiveness, reaching top-30 in Spain and top-500 worldwide on Hack The Box within a week, while dramatically reducing security testing costs by an average of 156x. Our framework transcends theoretical benchmarks by enabling non-professionals to discover significant security bugs (CVSS 4.3-7.5) at rates comparable to experts during bug bounty exercises. By combining modular agent design with seamless tool integration and human oversight (HITL), CAI addresses critical market gaps, offering organizations of all sizes access to AI-powered bug bounty security testing previously available only to well-resourced firms -thereby challenging the oligopolistic ecosystem currently dominated by major bug bounty platforms.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Hackers or Hallucinators? A Comprehensive Analysis of LLM-Based Automated Penetration Testing

    cs.CR 2026-04 unverdicted novelty 8.0

    The first SoK on LLM-based AutoPT frameworks provides a six-dimension taxonomy of agent designs and a unified empirical benchmark evaluating 15 frameworks via over 10 billion tokens and 1,500 manually reviewed logs.

  2. Certifying Ghosts: How Cybersecurity AI Agents Break the EU Cyber Resilience Act

    cs.CR 2026-07 conditional novelty 7.0

    Cybersecurity AI agents invalidate the CRA's premises about vulnerability discovery, exploitation, and remediation speed, making static conformity certificates false and requiring continuous agent-operated defense.

  3. Dynamic Cyber Ranges

    cs.CR 2026-04 unverdicted novelty 7.0

    Dynamic Cyber Ranges with LLM defender agents reduce attacker success to 0-55% and preserve evaluation headroom as models advance by using comparable capabilities on both sides.

  4. AI Agents Enable Adaptive Computer Worms

    cs.CR 2026-06 unverdicted novelty 6.0

    AI agents enable adaptive computer worms that propagate autonomously by reasoning about targets and synthesizing attacks using LLMs on stolen compute.

  5. Synthetic APTs: the Collapse of TTP-Based Attribution

    cs.CR 2026-06 unverdicted novelty 5.0

    AI-emulated APTs compromise enterprise hosts and weaponize defender tools in 8/10 trials while military ranges resist, indicating TTP attribution fails when agents can be scaffolded to mimic threat actors.