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%
< min
> %
dfbench: detection
Recall
Cost per task
dfbench: validation
Precision
Recall
dfbench: differential analysis
Macro recall
Cost per task
Every dfbench datapoint, by model and thinking level
| Thinking | |
|---|---|
| dfs-large1 | |
| GPT 5.6 Sol | xhigh |
| GPT 5.6 Sol | high |
| GPT 5.6 Sol | med |
| GPT 5.6 Luna | xhigh |
| GPT 5.6 Luna | high |
| GPT 5.6 Luna | med |
| Opus 5 | high |
| Opus 5 | med |
| Grok 4.5 | high |
| Kimi K3 | max |
| Kimi K3 | high |
| Qwen 3.8 | max |
| GLM 5.2 | xhigh |
| GLM 5.2 | high |
| DeepSeek v4 Flash | max |
| DeepSeek v4 Flash | high |
| Gemini 3.6 Flash | high |
| $6.77 | 62.2% |
| $43.37 | 65.7% |
| $26.47 | 59.6% |
| $9.51 | 48.9% |
| $2.53 | 52.4% |
| $1.57 | 39.6% |
| $0.69 | 28% |
| $22.66 | 45.4% |
| $21.89 | 47.8% |
| $7.70 | 57.2% |
| $9.10 | 48% |
| $6.22 | 46.7% |
| $7.91 | 40.8% |
| $9.72 | 40.7% |
| $4.09 | 38.2% |
| $1.23 | 31.8% |
| $1.03 | 30.1% |
| $7.44 | 20.9% |
| 66.5% | 42.4% | 62.3% |
| 67.1% | 67.3% | 60.7% |
| 62.8% | 62.3% | 49.2% |
| 54.2% | 38.5% | 44.3% |
| 56.8% | 45.1% | 47.5% |
| 48.4% | 23.1% | 31.1% |
| 31.6% | 15.4% | 29.5% |
| 54.8% | 34.1% | 31.1% |
| 45.8% | 57.7% | 44.3% |
| 59.1% | 51.6% | 0% |
| 31.8% | 31.8% | 30.4% |
| 48.4% | 51.8% | 37.9% |
| 40.8% | 40.8% | 40.8% |
| 34.8% | 22.4% | 44.3% |
| 42.6% | 11.1% | 44.3% |
| 27.6% | 29.5% | 45.9% |
| 30.1% | 30.1% | 30.1% |
| 19.4% | 11.5% | 32.8% |
| 63% | 52.6% |
| 66.3% | 57.9% |
| 63.3% | 10.5% |
| 50.6% | 26.3% |
| 53.2% | 42.1% |
| 40.2% | 31.6% |
| 29.3% | 10.5% |
| 47% | 26.3% |
| 48.2% | 42.1% |
| 59.9% | 25% |
| 32.3% | 21.1% |
| 48.5% | 31.6% |
| 40.8% | 40.8% |
| 35.6% | 20% |
| 42.2% | 36.8% |
| 32.9% | 26.3% |
| 31.9% | 8.3% |
| 20.9% | 21.1% |
| 18.3% |
| 18% |
| 22.3% |
| 24.6% |
| 23.2% |
| 24.8% |
| 27.3% |
| 39.4% |
| 40.5% |
| 29.5% |
| 30.1% |
| 33.3% |
| 29.6% |
| 28.6% |
| 30.8% |
| 24.8% |
| 26.8% |
| 43.5% |
| 28.3% |
| 28.3% |
| 32.5% |
| 32.7% |
| 32.2% |
| 30.5% |
| 27.6% |
| 42.2% |
| 43.8% |
| 38.9% |
| 37.0% |
| 38.9% |
| 34.3% |
| 33.6% |
| 34.1% |
| 27.9% |
| 28.4% |
| 28.2% |
| $1.34 | 75.6% |
| $10.67 | 75.3% |
| $5.46 | 76.3% |
| $2.32 | 74.4% |
| $0.62 | 71% |
| $0.30 | 71.3% |
| $0.13 | 65.1% |
| $7.41 | 70.3% |
| $5.48 | 71.3% |
| $3.38 | 65.1% |
| $6.34 | 60.3% |
| $4.18 | 60.1% |
| $2.99 | 72.7% |
| $2.04 | 61.4% |
| $1.56 | 58.6% |
| $0.79 | 66.8% |
| $0.55 | 67.4% |
| $1.51 | 61% |
| 45.8% | 95% | 86% |
| 46.8% | 95% | 84.1% |
| 45.6% | 94.4% | 88.9% |
| 39.2% | 93.3% | 90.7% |
| 38.9% | 89.3% | 84.8% |
| 32.5% | 92.4% | 89.1% |
| 22.2% | 79.2% | 94% |
| 38.2% | 79.8% | 92.8% |
| 37.4% | 83.8% | 92.8% |
| 39.9% | 74.5% | 80.9% |
| 35.5% | 72% | 73.4% |
| 31.4% | 75.3% | 73.5% |
| 33.6% | 95% | 89.6% |
| 25.5% | 85.6% | 73% |
| 25.3% | 76.4% | 74.1% |
| 28.4% | 83% | 89% |
| 27.1% | 88.1% | 87% |
| 18.3% | 68.9% | 96.5% |
ffmpeg
CVE-2026-39210
heap overflow in mpegts demuxer
nginx
CVE-2026-42533
pre-authentication heap overflow in stream script engine
apache httpd
CVE-2026-44186
remote worker dos in mod_proxy_ftp
openssh
CVE-2026-60002
use-after-free during host-key change on rekey
linux kernel
CVE-2026-31430
heap overflow in x.509 cert parser
chrome v8
CVE-2026-4457
type confusion
netty
CVE-2025-59419
smtp injection
nokogiri
CVE-2026-57434
null pointer dereference in uninitialized native node wrappers
sqlite3-ruby
CVE-2026-54620
use-after-free in aggregate function callbacks
chrome devtools
CVE-2026-3539
object lifecycle issue
temporal
CVE-2025-14986
cross-tenant metadata read, policy bypass
sandboxie
CVE-2025-64721
sandbox escape via heapo
Dependency Firewall blocks malicious packages. Security Reviewer validates every human and AI-generated code change before vulnerabilities, sensitive data, or malware enter your codebase.
Find real vulnerabilities by tracing business logic, data flows, and cross-service interactions across your codebase.
Detect malicious behavior before it spreads through your environment.
Confirm which vulnerabilities are exploitable by testing your running application with real attack paths.
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depthfirst felt like adding an autonomous senior product-security engineer. It quickly surfaced our top issues and got smarter over time by tracking context across scans, and it’s cut our security-engineering load by roughly 70%.
Since adopting General Security Intelligence and introducing its context-aware code reviewer into our workflow, we’ve increased our code security coverage by 2x. Its ability to learn from our patterns, understand application semantics, and continuously refine recommendations has been invaluable.
depthfirst has fundamentally changed how we think about code security and quality at Moveworks. They not only find code defects and complex threats; their PR reviewer proposes concrete code changes developers can apply directly in the pull request, making remediation fast and frictionless.
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