Modeling Sparse and Bursty Vulnerability Sightings: Forecasting Under Data Constraints
Paper • 2604.16038 • Published • 3
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cedricbonhomme
authored a
paper about 1 month ago
cedricbonhomme
submitted a
paper to Daily Papers about 1 month ago
jorgemunozl
posted an update 4 months ago
Post
414
Test
I know that it was buggy, OMG
I know that it was buggy, OMG
- 1 reply
cedricbonhomme
posted an update 5 months ago
Post
2798
With VLAgentIc, you can now use your local Qwen installation via Ollama and leverage the models CIRCL/vulnerability-severity-classification-roberta-base and CIRCL/cwe-parent-vulnerability-classification-roberta-base.
The project is available here:
https://github.com/vulnerability-lookup/VLAgentIc
The VLAI Severity and CWE classifiers are available on Hugging Face:
- CIRCL/vulnerability-severity-classification-roberta-base
- CIRCL/cwe-parent-vulnerability-classification-roberta-base
The concept of AI agents—combining models, tools, and orchestration—has become fairly standardized during the last year, but VLAgentIc brings something unique:
- Agents communicate over XMPP, enabling concurrent tasks and asynchronous messaging thanks to the SPADE framework.
- Built-in presence and discovery streamline interactions between components.
- Flexible behaviours make orchestrating AI-assisted security workflows seamless for future connections
- Last but not least, the VLAI Severity and VLAI CWE classifiers are now wrapped as LLM Tools and run entirely locally.
New, more comprehensive agent tools will soon be available, leveraging the Vulnerability-Lookup API and supporting the GCVE project.
The Human-in-the-Loop agent tool will be designed to notify you and request authorization whenever a query to an external service is about to be made—ensuring that, by default, all reasoning and processing stay local on your computer.
VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification (2507.03607)
The project is available here:
https://github.com/vulnerability-lookup/VLAgentIc
The VLAI Severity and CWE classifiers are available on Hugging Face:
- CIRCL/vulnerability-severity-classification-roberta-base
- CIRCL/cwe-parent-vulnerability-classification-roberta-base
The concept of AI agents—combining models, tools, and orchestration—has become fairly standardized during the last year, but VLAgentIc brings something unique:
- Agents communicate over XMPP, enabling concurrent tasks and asynchronous messaging thanks to the SPADE framework.
- Built-in presence and discovery streamline interactions between components.
- Flexible behaviours make orchestrating AI-assisted security workflows seamless for future connections
- Last but not least, the VLAI Severity and VLAI CWE classifiers are now wrapped as LLM Tools and run entirely locally.
New, more comprehensive agent tools will soon be available, leveraging the Vulnerability-Lookup API and supporting the GCVE project.
The Human-in-the-Loop agent tool will be designed to notify you and request authorization whenever a query to an external service is about to be made—ensuring that, by default, all reasoning and processing stay local on your computer.
VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification (2507.03607)
yuna0x0
authored a
paper 8 months ago
Post
685
Qwen 3 Coder is a personal attack to k2, and I love it.
It achieves near SOTA on LCB while not having reasoning.
Finally people are understanding that reasoning isnt necessary for high benches...
Qwen ftw!
DECENTRALIZE DECENTRALIZE DECENTRALIZE
It achieves near SOTA on LCB while not having reasoning.
Finally people are understanding that reasoning isnt necessary for high benches...
Qwen ftw!
DECENTRALIZE DECENTRALIZE DECENTRALIZE
cedricbonhomme
authored a
paper 11 months ago
Post
3162
deepseek-ai/DeepSeek-R1-0528
This is the end
This is the end
- 1 reply
asa1108
published a
model about 1 year ago
asa1108
updated a
model about 1 year ago
Post
4390
There seems to multiple paid apps shared here that are based on models on hf, but some ppl sell their wrappers as "products" and promote them here. For a long time, hf was the best and only platform to do oss model stuff but with the recent AI website builders anyone can create a product (really crappy ones btw) and try to sell it with no contribution to oss stuff. Please dont do this, or try finetuning the models you use...
Sorry for filling yall feed with this bs but yk...
Sorry for filling yall feed with this bs but yk...
- 6 replies
Post
1679
Gemma 3 seems to be really good at human preference. Just waiting for ppl to see it.
ameerazam08
posted an update over 1 year ago
Post
1665
R1 is out! And with a lot of other R1 releated models...
JoseRFJunior
posted an update almost 2 years ago
Post
1742
JoseRFJunior/TransNAR
https://github.com/JoseRFJuniorLLMs/TransNAR
https://arxiv.org/html/2406.09308v1
TransNAR hybrid architecture. Similar to Alayrac et al, we interleave existing Transformer layers with gated cross-attention layers which enable information to flow from the NAR to the Transformer. We generate queries from tokens while we obtain keys and values from nodes and edges of the graph. The node and edge embeddings are obtained by running the NAR on the graph version of the reasoning task to be solved. When experimenting with pre-trained Transformers, we initially close the cross-attention gate, in order to fully preserve the language model’s internal knowledge at the beginning of training.
https://github.com/JoseRFJuniorLLMs/TransNAR
https://arxiv.org/html/2406.09308v1
TransNAR hybrid architecture. Similar to Alayrac et al, we interleave existing Transformer layers with gated cross-attention layers which enable information to flow from the NAR to the Transformer. We generate queries from tokens while we obtain keys and values from nodes and edges of the graph. The node and edge embeddings are obtained by running the NAR on the graph version of the reasoning task to be solved. When experimenting with pre-trained Transformers, we initially close the cross-attention gate, in order to fully preserve the language model’s internal knowledge at the beginning of training.