ProjectDiscovery ✓

@pdiscoveryio · joined 28 Jul 2019

Real, exploitable vulnerabilities. No noise. Nuclei scans fast. Neo closes the loop. @pdnuclei × @neo_ai_engineer

43 679Followers

144Following

2 945Posts total

176.8KViews on collected posts

Latest posts

ProjectDiscovery ✓@pdiscoveryio21 Sep

something is coming soon for hackers and dreamers. https://t.co/qyQHE0SUTs

0:18

4K views · 77 likes · 12 reposts · 1 replies Open on X →

ProjectDiscovery ✓@pdiscoveryio19 Sep

“A one-week job… to a few hours.” NahamSec shares how AI changed his hacking workflow from reviewing source code to catching things he might have missed during recon. Watch full conversation👇 https://t.co/hlIhgZhxVr

1:00

2.9K views · 21 likes · 1 reposts · 0 replies Open on X →

ProjectDiscovery ✓@pdiscoveryio18 Sep

https://t.co/9V520l8oSf 2.3K views · 16 likes · 4 reposts · 0 replies Open on X →

ProjectDiscovery ✓@pdiscoveryio17 Sep

How does reward hacking during training turn into a rogue model? How did this lead to the OpenAI Hugging Face incident? In this conversation, @ehrishiraj and @KoyalwarTarun unpack what the early reports missed: the role of misalignment during training, and how reward hacking

23:33

2.5K views · 17 likes · 3 reposts · 2 replies Open on X →

ProjectDiscovery ✓@pdiscoveryio17 Sep

@pdiscoveryio @NahamSec @ehrishiraj Finding a bug faster is useful. Finding the same bug faster has a different payout. 26 views · 0 likes · 0 reposts · 0 replies Open on X →

ProjectDiscovery ✓@pdiscoveryio16 Sep

How is AI changing bug bounty hunting, and where do human hackers still make the difference? @NahamSec and @ehrishiraj discuss AI agents, ethical hacking, and the future of vulnerability research from reconnaissance and code review to building security agents and verifying http

63:29

6K views · 26 likes · 9 reposts · 1 replies Open on X →

ProjectDiscovery ✓@pdiscoveryio14 Sep

@pdiscoveryio The useful distinction isn't rogue agent vs bad eval. In both cases, defenders need the execution chain, blast radius, and a clearly assigned owner. That's where AI incident response starts. 49 views · 1 likes · 0 reposts · 0 replies Open on X →

ProjectDiscovery ✓@pdiscoveryio14 Sep

@pdiscoveryio The key lesson for defenders is that AI-assisted attacks can evolve through feedback, not just execute a fixed playbook Detecting the attack’s adaptation loop may matter more than detecting any single payload. 54 views · 0 likes · 0 reposts · 0 replies Open on X →

ProjectDiscovery ✓@pdiscoveryio14 Sep

@pdiscoveryio does this cover off the failures around (missing) controls in the test environments? 388 views · 2 likes · 0 reposts · 1 replies Open on X →

ProjectDiscovery ✓@pdiscoveryio14 Sep

Too much has been written about the OpenAI and Hugging Face incident to keep up with. We wrote the simplified version for security researchers and engineers: -> what actually happened inside training? -> how the attack evolved? ->what it means for defenders? Read: https://t.c

158.5K views · 54 likes · 15 reposts · 3 replies Open on X →

Against accounts of the same size

10 posts from the last 90 days, next to the 10K–100K follower range. shown widely, but few of those viewers react.

Median views2 418this account927median for 10K–100K

Reach, %5.53%this account3.63%median for 10K–100K

Engagement, %0.77%this account1.52%median for 10K–100K

Metric	This account	Median for 10K–100K	Ratio
Median views per post	2 418	927	2.61×
Reach (views ÷ followers)	5.53%	3.63%	1.52×
Engagement rate	0.77%	1.52%	0.51×

Others in this range → Compare with another account → How these benchmarks are built →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

158.5K14 Sep

388

6K16 Sep

2617 Sep

2.5K

2.3K18 Sep

2.9K19 Sep

4K21 Sep

Last 10 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.05%14 Sep

0.77%

0.00%

2.04%

0.63%16 Sep

0.00%17 Sep

0.91%

0.87%18 Sep

0.77%19 Sep

2.24%21 Sep

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.

What the audience does

Likes60.3%214 in total

Reposts12.4%44 in total

Replies2.3%8 in total

Quotes2.3%8 in total

Bookmarks22.8%81 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

Latest posts

Against accounts of the same size

Growth & engagement

Views per post

Engagement rate per post

What the audience does

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