On July 23–24, 2026, researcher Chaofan Shou (posting as @Fried_rice and operating under Bera Buddies) published a demonstration that has set the security community buzzing: 32 coordinated Kimi K3 AI sub-agents autonomously found 19 previously unknown vulnerabilities in Redis and built working exploit proof-of-concepts — all within roughly 90 minutes.

This is a remarkable result. It’s also an important one to interpret carefully, because the headline numbers come with significant caveats that context demands.

What Happened

The core experiment: Shou deployed 32 parallel Kimi K3 agents against a Redis codebase. The agents operated with minimal human intervention, performing a security research workflow that traditionally requires days of skilled manual effort:

  1. Cloning the Redis source repository
  2. Building custom fuzz harnesses
  3. Instrumenting the code for crash analysis
  4. Debugging crash outputs with GDB
  5. Producing working PoC exploit code

In a shorter run, the agents produced a working authenticated remote code execution (RCE) exploit in 27 minutes. In a longer run, they identified 19 zero-days in approximately 90 minutes. The PoC code was published to GitHub at berabuddies/redis-poc.

Redis responded quickly: on July 23, the project shipped seven security patch releases across multiple branches, including 6.2.23, 7.4.10, 8.6.5, and 8.8.1. The affected versions were Redis 6.2.22, 7.4.9, 8.6.4, and 8.8.0.

The Technical Details

The disclosed issues center on two main vulnerability classes:

  • Shared-NACK use-after-free / double-free in Redis Streams: triggered through corrupt RDB objects during RESTORE operations, potentially enabling memory corruption
  • Out-of-bounds writes in RedisBloom’s TDigest module: heap overflow / memory corruption during RDB loading

These vulnerabilities, when chained with commands including RESTORE, EVAL, and XGROUP, can enable RCE on affected versions. Redis patched the issues with guards against duplicate ownership and bounds checks.

The Caveats — And Why They Matter

This story requires an honest accounting of what the demonstration did and did not show. The Analyst flagged three critical caveats; readers deserve them front and center:

1. Authenticated RCE only. Every exploit produced requires valid Redis credentials first. These are not unauthenticated network attacks. Redis is explicitly designed not to be exposed to untrusted networks, and in properly deployed Redis instances, an attacker would need prior access to exploit these vulnerabilities. This makes them significantly less severe than unauthenticated RCEs — though still real vulnerabilities worth patching.

2. Self-reported, no CVEs assigned. As of July 24, Chaofan Shou has not undergone a formal coordinated disclosure process with CVE assignments. The claims about timing (27 minutes, 90 minutes, 19 zero-days) are from the researcher’s own X posts and have not been independently reproduced or formally verified by Redis maintainers beyond the fact that they issued patches.

3. Community skepticism about the “from scratch” framing. Discussion on HN and in security circles has raised questions about whether the agents genuinely discovered these patterns from scratch or whether they leveraged known Redis memory corruption patterns from their training data. This is a legitimate methodological question. The answer matters for how we interpret the claim of “autonomous discovery.”

None of this erases the significance of what happened. Redis patched real issues that the agents found. But calling this a “landmark demonstration of autonomous AI vulnerability discovery” requires understanding what was and wasn’t demonstrated.

The Bigger Picture: Multi-Agent Offensive Security

What this experiment does unambiguously show — even accounting for the caveats — is that multi-agent AI systems can compress security research timelines. Whether the agents are “discovering” vulnerabilities in a novel sense or pattern-matching against deeply trained knowledge, they are producing real, patchable security findings faster than traditional manual processes.

This pairs naturally as the offensive counterpart to Visa’s VVAH pipeline (covered separately), which is building AI-powered defensive tooling for vulnerability triage and remediation. The same week that shows AI agents finding exploits at machine speed is also the week a major financial institution open-sources a production-grade AI system for fixing them.

For practitioners: if you are running Redis instances, apply the July 23 patches now. For the broader security community: this demonstration — caveats included — represents a turning point in how seriously we should take AI-assisted offensive security research.

Sources

  1. Kimi K3 Agents Found Redis Zero-Days and Built RCE Exploit — The Hacker News
  2. Chaofan Shou (@Fried_rice) — 27-minute RCE post
  3. Chaofan Shou (@Fried_rice) — 19 zero-days in 90 minutes
  4. PoC repository: berabuddies/redis-poc
  5. RuntimeWire coverage

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260724-2000

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