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Index › Security operations › PTaaS › UnderDefense vs HackerOne
Penetration testing as a service · October 2026 Edition

UnderDefense vs HackerOne

Two of fourteen models named UnderDefense first on the direct prompt; zero named HackerOne. UnderDefense was named by seven of the fourteen models and HackerOne by ten and UnderDefense carries 11 labels and HackerOne 14, so the shares are not directly comparable.

UnderDefense

accepted challenger

Named in four categories this edition.

HackerOne

accepted challenger

Named in two categories this edition.

First-choice share4%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%14%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#5#6A position in a field of 10; printed, not drawn.
Labels1114A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, UnderDefense reading right to left. Rank and label count are printed, not drawn.Cobalt was named alongside these two in ten of the fourteen direct answers. Cobalt vs UnderDefense · Cobalt vs HackerOne · BreachLock vs UnderDefense

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the penetration testing as a service page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
UnderDefenseFirst choices, of fourteen modelsHackerOne
Direct201 against HackerOne
Paraphrase001 against HackerOne
Comparative00
Budget-constrained01
Scale-constrained00
Negative00
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where UnderDefense and HackerOne stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
Claude Haiku 4.5
GPT-5.4 mini
Gemini 3.5 Flash
Perplexity Sonar
Grok 4.1 Fast
Mistral Small
DeepSeek V4 Flash
Llama 4 Maverick
Qwen 3.7 Flash
Kimi K2
GLM 4.7 FlashX
MiniMax M2.5
GPT-6 Luna
Muse Glimmer 30B
UnderDefense HackerOne first choice named as an alternative argued againstblank: not namedEach cell is one answer, UnderDefense on the left and HackerOne on the right.

The direct prompt

The plain question, one answer per model, grouped by where UnderDefense and HackerOne stood in it.

UnderDefense first, HackerOne not the choice

2 of 14 modelsHackerOne was named in the answer but not as the choice, or not at all.
GLM 4.7 FlashXCobalt, UnderDefense alternatives: BreachLock, NetSPI, Packetlabs
MiniMax M2.5BreachLock, UnderDefense alternatives: Raxis, Synack

Neither was the first choice, one was named

3 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashCobalt alternatives: BreachLock, NetSPI, UnderDefense
DeepSeek V4 FlashCobalt, Raxis alternatives: BreachLock, Packetlabs, UnderDefense
Muse Glimmer 30BCobalt alternatives: Astra Security, HackerOne, Netragard, Red Siege, UnderDefense

Neither was named

9 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5BreachLock, Cobalt alternatives: Packetlabs, Raxis, Stingrai
GPT-5.4 miniCobalt alternatives: Cognisys, Trava Security
Perplexity SonarBreachLock alternatives: Cobalt, Raxis, Stingrai
Grok 4.1 FastBreachLock alternatives: Bright Defense, NetSPI, Packetlabs, Prescient Security
Mistral SmallBreachLock, Cobalt alternatives: NetSPI, Stingrai, Synack
Llama 4 Maverickno first choice
Qwen 3.7 FlashRed Siege Security alternatives: Astra Security, BreachLock, Lumina.io, Netragard
Kimi K2Cobalt alternatives: BreachLock, Coalfire, Praetorian
GPT-6 LunaCobalt alternatives: Bishop Fox, NetSPI

Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.

By buyer segment

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
UnderDefense leads by two points.
UnderDefense2%#6 of 8
HackerOne0%#– of 8
The full small business standing →
Mid-marketThe figures above
UnderDefense leads by two points.
UnderDefense4%#5 of 10
HackerOne2%#6 of 10
The full mid-market standing →
Enterprise
Level: the same share of first choices.
UnderDefense0%#– of 11
HackerOne0%#10 of 11
The full enterprise standing →

What the models said about UnderDefense

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Two of two in this category shown.

“I'd recommend BreachLock or UnderDefense as they specifically target mid-market companies” MiniMax M2.5 · direct prompt · first choice
“UnderDefense or Cobalt offer the best balance of pricing, quality, and scalability” GLM 4.7 FlashX · direct prompt · first choice

What the models said about HackerOne

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.

“crowdsourced like HackerOne/Cobalt work for speed but less for deep compliance” Grok 4.1 Fast · direct prompt · soft negative
“often more suited to larger enterprises or those with more complex needs” Mistral Small · paraphrase prompt · soft negative
“Platforms like HackerOne, Bugcrowd, or Open Bug Bounty allow you to set a budget and pay only for valid vulnerabilities found” MiniMax M2.5 · budget prompt · first choice
“Mid-Tier Specialist (e.g., Cobalt, HackerOne, Antisyphon) – Platform-based, often better balance of cost and quality.” Qwen 3.7 Flash · scale prompt · alternative
“HackerOne if you already run, or plan to run, a bug bounty / vulnerability disclosure program” GPT-5.4 mini · paraphrase prompt · alternative
Also compared

Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.