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Lower cost than frontier harness + models at equivalent performance

Our customers choose depthfirst to stay ahead of how software is built and attacked.

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%.

Our teams use the entire depthfirst suite. We have Dependency Firewall, PR reviews, code reviews, and Agentic Pentesting. That was what brought me to depthfirst: it covers the full cycle.

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.

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%.

Our teams use the entire depthfirst suite. We have Dependency Firewall, PR reviews, code reviews, and Agentic Pentesting. That was what brought me to depthfirst: it covers the full cycle.

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.

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%.

Our teams use the entire depthfirst suite. We have Dependency Firewall, PR reviews, code reviews, and Agentic Pentesting. That was what brought me to depthfirst: it covers the full cycle.

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.

One intelligent platform to detect, validate, and remediate security issues across your software.

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Detection Validation Differential analysis

dfbench: detection

Pareto frontier

Recall

20% 30% 40% 50% 60% 70% $0.30 $1 $3 $10 $30 $100 dfs-large1 GPT 5.6 Sol GPT 5.6 Luna Opus 5 Opus 5.5 Grok 4.5 Grok 4.7 Kimi K3 Qwen 3.8 GLM 5.2 DeepSeek v4 Flash Gemini 3.6 Flash Mythos 5 Gemini 3.8 Flash Muse Spark 1.3 DeepSeek v4.1 Flash GPT 6 Luna GPT 6 Sol MiMo-v2.6 Flash MiMo-v2.6 Pro

Cost per task

dfs-large1
GPT 5.6 Sol
GPT 5.6 Luna
Opus 5
Opus 5.5
Grok 4.5
Grok 4.7
Kimi K3
Qwen 3.8
GLM 5.2
DeepSeek v4 Flash
Gemini 3.6 Flash

Mythos 5\

Gemini 3.8 Flash
Muse Spark 1.3
DeepSeek v4.1 Flash
GPT 6 Luna
GPT 6 Sol
MiMo-v2.6 Flash
MiMo-v2.6 Pro

Figure 1: Vulnerability recall across a security scope, against cost per task. The ideal agent sits in the top left corner. Unless otherwise specified, all models are run on our internal harness.

dfbench: validation

Pareto frontier

Precision

20% 25% 30% 35% 40% 45% 50% 20% 30% 40% 50% 60% 70% dfs-large1 GPT 5.6 Sol GPT 5.6 Luna Opus 5 Opus 5.5 Grok 4.5 Grok 4.7 Kimi K3 Qwen 3.8 GLM 5.2 DeepSeek v4 Flash Gemini 3.6 Flash Mythos 5 Gemini 3.8 Flash Muse Spark 1.3 DeepSeek v4.1 Flash GPT 6 Luna GPT 6 Sol MiMo-v2.6 Flash MiMo-v2.6 Pro

Recall

dfs-large1
GPT 5.6 Sol
GPT 5.6 Luna
Opus 5
Opus 5.5
Grok 4.5
Grok 4.7
Kimi K3
Qwen 3.8
GLM 5.2
DeepSeek v4 Flash
Gemini 3.6 Flash

Mythos 5\

Gemini 3.8 Flash
Muse Spark 1.3
DeepSeek v4.1 Flash
GPT 6 Luna
GPT 6 Sol
MiMo-v2.6 Flash
MiMo-v2.6 Pro

Figure 2: Precision of an agent's findings, against detection recall. The ideal agent sits in the top right corner.

dfbench: differential analysis

Pareto frontier

Macro recall

55% 60% 65% 70% 75% 80% $0.10 $0.30 $1 $3 $10 dfs-large1 GPT 5.6 Sol GPT 5.6 Luna Opus 5 Opus 5.5 Grok 4.5 Grok 4.7 Kimi K3 Qwen 3.8 GLM 5.2 DeepSeek v4 Flash Gemini 3.6 Flash Gemini 3.8 Flash Muse Spark 1.3 DeepSeek v4.1 Flash GPT 6 Luna GPT 6 Sol MiMo-v2.6 Flash MiMo-v2.6 Pro

Cost per task

dfs-large1
GPT 5.6 Sol
GPT 5.6 Luna
Opus 5
Opus 5.5
Grok 4.5
Grok 4.7
Kimi K3
Qwen 3.8
GLM 5.2
DeepSeek v4 Flash
Gemini 3.6 Flash
Gemini 3.8 Flash
Muse Spark 1.3
DeepSeek v4.1 Flash
GPT 6 Luna
GPT 6 Sol
MiMo-v2.6 Flash
MiMo-v2.6 Pro

Figure 3: Macro recall after advancing a scope from its base commit to its target commit, against cost per task. The ideal agent sits in the top left corner.

Building a Faster, Cheaper Vulnerability Scanner With Jev Containers Are No Longer a Security Boundary Introducing dfbench v1: A cybersecurity benchmark for frontier models and agentic systems Introducing dfs-large1: A New Frontier for Cybersecurity Behind the GitLab RCE: A depthfirst Journey into the Ruby Ecosystem Going depthfirst: Achieving GitLab RCE via Two Ruby Memory Corruption Vulnerabilities

Every dfbench datapoint, by model and thinking level

| Model and thinking level | Model | 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 | | | Opus 5.5 | high | | | Opus 5.5 | med | | | Grok 4.5 | high | | | Grok 4.7 | xhigh | | | Grok 4.7 | 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 | | | Mythos 5\* | | | | Gemini 3.8 Flash | high | | | Muse Spark 1.3 | xhigh | | | DeepSeek v4.1 Flash | high | | | GPT 6 Luna | xhigh | | | GPT 6 Luna | high | | | GPT 6 Luna | med | | | GPT 6 Sol | xhigh | | | GPT 6 Sol | high | | | GPT 6 Sol | med | | | MiMo-v2.6 Flash | | | | MiMo-v2.6 Pro | | |

| Detection cost and overall recall | Detection Domain Scope | | --------------------------------- | ----------------------- | | Cost / task | Overall recall | | $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% | | $8.42 | 54.9% | | $7.66 | 48.1% | | $7.70 | 57.2% | | $19.94 | 59.0% | | $16.02 | 56.6% | | $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% | | $99.19 | 69% | | $8.03 | 44.2% | | $10.24 | 47.4% | | $1.69 | 57.3% | | $0.43 | 45.7% | | $0.28 | 38.1% | | $0.15 | 37.3% | | $13.30 | 59.3% | | $6.45 | 57.7% | | $3.43 | 49.6% | | $0.86 | 53.5% | | $1.38 | 55% |

| Detection recall by domain | | | | -------------------------- | --------- | -------------- | | Application | Low level | Smart contract | | 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% | | 60.9% | — | 48.2% | | 57.5% | — | 35.9% | | 59.1% | 51.6% | 0% | | 68.4% | 42.1% | 53.9% | | 68.4% | 15.8% | 55.1% | | 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% | | 70.8% | 61.4% | 71% | | 50.6% | 17.3% | 50.8% | | 55.5% | 32% | 39.3% | | 64.1% | 54.5% | 48.9% | | 50% | 40.4% | 39.3% | | 43.9% | 25% | 34.4% | | 40.6% | 26.9% | 37.7% | | 61.9% | 51.9% | 59% | | 58.7% | 56.9% | 55.7% | | 48.4% | 51.9% | 50.8% | | 54.9% | 47.9% | 55.3% | | 58.6% | 38.9% | 59.8% |

| Detection recall by repository scope | | | ------------------------------------ | ---------- | | Single repo | Multi-repo | | 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% | | 57.2% | 31.3% | | 49.7% | 31.6% | | 59.9% | 25% | | 61.4% | 20% | | 57.7% | 40% | | 49.8% | 34.8% | | 48.5% | 31.6% | | 40.8% | 40.8% | | 43.4% | 20.9% | | 42.2% | 36.8% | | 32.9% | 26.3% | | 31.9% | 8.3% | | 20.9% | 21.1% | | 70.4% | 50% | | 44.8% | 36.3% | | 48.6% | 31.6% | | 58.5% | 42.1% | | 46.4% | 36.8% | | 39.4% | 21.1% | | 38.6% | 21.1% | | 59.4% | 57.9% | | 58.9% | 42.1% | | 50.2% | 42.1% | | 54.3% | 28.6% | | — | — |

| Validation precision | Validation | | -------------------- | ---------- | | Precision | | | 18.3% | | | 18% | | | 22.3% | | | 24.6% | | | 23.2% | | | 24.8% | | | 27.3% | | | 39.4% | | | 40.5% | | | 48.5% | | | 44.2% | | | 29.5% | | | 23.9% | | | 30.5% | | | 30.1% | | | 33.3% | | | 29.6% | | | 28.6% | | | 30.8% | | | 24.8% | | | 26.8% | | | 43.5% | | | 24.5% | | | 41.4% | | | 34.1% | | | 36.4% | | | 21.8% | | | 24.5% | | | 33.3% | | | 15.7% | | | 18.9% | | | 22.6% | | | 24.4% | | | 18.1% | |

| F1 from detect recall and validate precision | | | -------------------------------------------- | | | F1 | | | 28.3% | | | 28.3% | | | 32.5% | | | 32.7% | | | 32.2% | | | 30.5% | | | 27.6% | | | 42.2% | | | 43.8% | | | 51.5% | | | 46.1% | | | 38.9% | | | 34.0% | | | 39.7% | | | 37.0% | | | 38.9% | | | 34.3% | | | 33.6% | | | 34.1% | | | 27.9% | | | 28.4% | | | 28.2% | | | 36.2% | | | 42.8% | | | 39.7% | | | 44.5% | | | 29.5% | | | 29.8% | | | 35.2% | | | 24.8% | | | 28.5% | | | 31.1% | | | 33.5% | | | 27.2% | |

| Differential analysis | Differential analysis | | --------------------- | --------------------- | | Cost / task | Macro recall | | $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% | | $1.78 | 79.5% | | $1.34 | 75.3% | | $3.38 | 65.1% | | $6.56 | 74.8% | | $6.46 | 70.3% | | $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% | | — | — | | $1.57 | 63.8% | | $3.12 | 72.8% | | $0.71 | 70.1% | | $0.12 | 72.1% | | $0.09 | 72.4% | | $0.07 | 69.5% | | $3.90 | 75.2% | | $2.06 | 76.3% | | $1.00 | 71.6% | | $0.08 | 72.7% | | $0.12 | 76.5% |

| Differential analysis by NEW, KEPT, and CLOSED | | | | ---------------------------------------------- | ----- | ------ | | NEW | KEPT | CLOSED | | 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% | | 66.7% | 86.8% | 85.2% | | 47.7% | 91.5% | 86.8% | | 39.9% | 74.5% | 80.9% | | 51.2% | 90.5% | 82.6% | | 43.9% | 86.3% | 80.8% | | 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% | | — | — | — | | 32% | 66.3% | 93.1% | | 47.1% | 85.1% | 86.2% | | 48.5% | 67.5% | 94.2% | | 44.4% | 89% | 82.9% | | 45.8% | 91.2% | 80.1% | | 36.1% | 87.4% | 85% | | 58.3% | 92.9% | 74.5% | | 61.8% | 92% | 75% | | 52.8% | 86.3% | 75.9% | | 38.7% | 89.9% | 89.5% | | 55.6% | 92.8% | 81.3% |

These results will be updated as new models and harnesses are released. Expand detect to see results stratified by example type. \* Run with Claude Security.

18 456789 vulnerabilities validated

596061626364 open-source projects

heap overflow in mpegts demuxer pre-authentication heap overflow in stream script engine remote worker dos in mod\_proxy\_ftp use-after-free during host-key change on rekey heap overflow in x.509 cert parser type confusion smtp injection null pointer dereference in uninitialized native node wrappers use-after-free in aggregate function callbacks object lifecycle issue cross-tenant metadata read, policy bypass sandbox escape via heapo

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