ExploitGym AI benchmark source code
ExploitGym ExploitGym is a large-scale, realistic benchmark built from real-world vulnerabilities across userspace programs, Google's V8 engine, and the Linux kernel, designed to evaluate AI agents' ability to develop exploits. Build runtime artifacts (gdb, socat, nc, node + agent CLIs) and # extract task data bash scripts/setup/setup_data.sh # 3. Verify the install bash scripts/setup/validate.sh # 4.
- ▪ExploitGym ExploitGym is a large-scale, realistic benchmark built from real-world vulnerabilities across userspace programs, Google's V8 engine, and the Linux kernel, designed to evaluate AI agents' ability to develop exploits.
- ▪Build runtime artifacts (gdb, socat, nc, node + agent CLIs) and # extract task data bash scripts/setup/setup_data.sh # 3.
- ▪Verify the install bash scripts/setup/validate.sh # 4.
Hacker News (AI / LLM) files mainly under ai. We currently carry 2,700 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
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Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/sunblaze-ucb/exploitgym/ |
| Publication time | Wed, 29 Jul 2026 07:40:13 +0000 |
| Retrieval time | 2026-07-29T07:46:40.526Z |
| Last seen | 2026-07-29T07:46:40.526Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | wLv4isLKabr0 · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
ExploitGym ExploitGym is a large-scale, realistic benchmark built from real-world vulnerabilities across userspace programs, Google's V8 engine, and the Linux kernel, designed to evaluate AI agents' ability to develop exploits. Quick start # 1. Python deps uv sync --extra proxy # 2. Build runtime artifacts (gdb, socat, nc, node + agent CLIs) and # extract task data bash scripts/setup/setup_data.sh # 3. Verify the install bash scripts/setup/validate.sh # 4. Pull the Firewall Squid image docker pull ubuntu/squid:latest # 5. Pull the Docker images for the tasks you want to run uv run scripts/setup/pull_images.py data/task_ids/sample.txt # 6. Start the controller, firewall, and LLM proxy.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.