#hacknews — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #hacknews, aggregated by home.social.
-
🔥🚀 Oh, rejoice! Another day, another hack—this time, Bitwarden's CLI couldn't dodge a bullet in the #Checkmarx supply chain campaign. Thank goodness for Socket Research Team, because without them, we'd never know which npm package will ruin our day next! 🙄🔒💥
https://socket.dev/blog/bitwarden-cli-compromised #Bitwarden #SupplyChain #SocketResearch #npmSecurity #HackNews #HackerNews #ngated -
🔥🚀 Oh, rejoice! Another day, another hack—this time, Bitwarden's CLI couldn't dodge a bullet in the #Checkmarx supply chain campaign. Thank goodness for Socket Research Team, because without them, we'd never know which npm package will ruin our day next! 🙄🔒💥
https://socket.dev/blog/bitwarden-cli-compromised #Bitwarden #SupplyChain #SocketResearch #npmSecurity #HackNews #HackerNews #ngated -
🔥🚀 Oh, rejoice! Another day, another hack—this time, Bitwarden's CLI couldn't dodge a bullet in the #Checkmarx supply chain campaign. Thank goodness for Socket Research Team, because without them, we'd never know which npm package will ruin our day next! 🙄🔒💥
https://socket.dev/blog/bitwarden-cli-compromised #Bitwarden #SupplyChain #SocketResearch #npmSecurity #HackNews #HackerNews #ngated -
🔥🚀 Oh, rejoice! Another day, another hack—this time, Bitwarden's CLI couldn't dodge a bullet in the #Checkmarx supply chain campaign. Thank goodness for Socket Research Team, because without them, we'd never know which npm package will ruin our day next! 🙄🔒💥
https://socket.dev/blog/bitwarden-cli-compromised #Bitwarden #SupplyChain #SocketResearch #npmSecurity #HackNews #HackerNews #ngated -
🔥🚀 Oh, rejoice! Another day, another hack—this time, Bitwarden's CLI couldn't dodge a bullet in the #Checkmarx supply chain campaign. Thank goodness for Socket Research Team, because without them, we'd never know which npm package will ruin our day next! 🙄🔒💥
https://socket.dev/blog/bitwarden-cli-compromised #Bitwarden #SupplyChain #SocketResearch #npmSecurity #HackNews #HackerNews #ngated -
Large-scale online deanonymization with LLMs
From Cornel University Computer ScienceWe show that large language models can be used to perform at-scale deanonymization. With full Internet access, our agent can re-identify Hacker News users and Anthropic Interviewer participants at high precision, given pseudonymous online profiles and conversations alone, matching what would take hours for a dedicated human investigator. We then design attacks for the closed-world setting. Given two databases of pseudonymous individuals, each containing unstructured text written by or about that individual, we implement a scalable attack pipeline that uses LLMs to: (1) extract identity-relevant features, (2) search for candidate matches via semantic embeddings, and (3) reason over top candidates to verify matches and reduce false positives. Compared to classical deanonymization work (e.g., on the Netflix prize) that required structured data, our approach works directly on raw user content across arbitrary platforms. We construct three datasets with known ground-truth data to evaluate our attacks. The first links Hacker News to LinkedIn profiles, using cross-platform references that appear in the profiles. Our second dataset matches users across Reddit movie discussion communities; and the third splits a single user's Reddit history in time to create two pseudonymous profiles to be matched. In each setting, LLM-based methods substantially outperform classical baselines, achieving up to 68% recall at 90% precision compared to near 0% for the best non-LLM method. Our results show that the practical obscurity protecting pseudonymous users online no longer holds and that threat models for online privacy need to be reconsidered.
Simon Lermen, Daniel Paleka, Joshua Swanson, Michael Aerni, Nicholas Carlini, Florian Tramè
#computerscience #cornelluniversity #AiResearch #privacy #anonymity #llm #HackNews #athropic #pseudonymity
#deanonymization -
Large-scale online deanonymization with LLMs
From Cornel University Computer ScienceWe show that large language models can be used to perform at-scale deanonymization. With full Internet access, our agent can re-identify Hacker News users and Anthropic Interviewer participants at high precision, given pseudonymous online profiles and conversations alone, matching what would take hours for a dedicated human investigator. We then design attacks for the closed-world setting. Given two databases of pseudonymous individuals, each containing unstructured text written by or about that individual, we implement a scalable attack pipeline that uses LLMs to: (1) extract identity-relevant features, (2) search for candidate matches via semantic embeddings, and (3) reason over top candidates to verify matches and reduce false positives. Compared to classical deanonymization work (e.g., on the Netflix prize) that required structured data, our approach works directly on raw user content across arbitrary platforms. We construct three datasets with known ground-truth data to evaluate our attacks. The first links Hacker News to LinkedIn profiles, using cross-platform references that appear in the profiles. Our second dataset matches users across Reddit movie discussion communities; and the third splits a single user's Reddit history in time to create two pseudonymous profiles to be matched. In each setting, LLM-based methods substantially outperform classical baselines, achieving up to 68% recall at 90% precision compared to near 0% for the best non-LLM method. Our results show that the practical obscurity protecting pseudonymous users online no longer holds and that threat models for online privacy need to be reconsidered.
Simon Lermen, Daniel Paleka, Joshua Swanson, Michael Aerni, Nicholas Carlini, Florian Tramè
#computerscience #cornelluniversity #AiResearch #privacy #anonymity #llm #HackNews #athropic #pseudonymity
#deanonymization -
Large-scale online deanonymization with LLMs
From Cornel University Computer ScienceWe show that large language models can be used to perform at-scale deanonymization. With full Internet access, our agent can re-identify Hacker News users and Anthropic Interviewer participants at high precision, given pseudonymous online profiles and conversations alone, matching what would take hours for a dedicated human investigator. We then design attacks for the closed-world setting. Given two databases of pseudonymous individuals, each containing unstructured text written by or about that individual, we implement a scalable attack pipeline that uses LLMs to: (1) extract identity-relevant features, (2) search for candidate matches via semantic embeddings, and (3) reason over top candidates to verify matches and reduce false positives. Compared to classical deanonymization work (e.g., on the Netflix prize) that required structured data, our approach works directly on raw user content across arbitrary platforms. We construct three datasets with known ground-truth data to evaluate our attacks. The first links Hacker News to LinkedIn profiles, using cross-platform references that appear in the profiles. Our second dataset matches users across Reddit movie discussion communities; and the third splits a single user's Reddit history in time to create two pseudonymous profiles to be matched. In each setting, LLM-based methods substantially outperform classical baselines, achieving up to 68% recall at 90% precision compared to near 0% for the best non-LLM method. Our results show that the practical obscurity protecting pseudonymous users online no longer holds and that threat models for online privacy need to be reconsidered.
Simon Lermen, Daniel Paleka, Joshua Swanson, Michael Aerni, Nicholas Carlini, Florian Tramè
#computerscience #cornelluniversity #AiResearch #privacy #anonymity #llm #HackNews #athropic #pseudonymity
#deanonymization -
Large-scale online deanonymization with LLMs
From Cornel University Computer ScienceWe show that large language models can be used to perform at-scale deanonymization. With full Internet access, our agent can re-identify Hacker News users and Anthropic Interviewer participants at high precision, given pseudonymous online profiles and conversations alone, matching what would take hours for a dedicated human investigator. We then design attacks for the closed-world setting. Given two databases of pseudonymous individuals, each containing unstructured text written by or about that individual, we implement a scalable attack pipeline that uses LLMs to: (1) extract identity-relevant features, (2) search for candidate matches via semantic embeddings, and (3) reason over top candidates to verify matches and reduce false positives. Compared to classical deanonymization work (e.g., on the Netflix prize) that required structured data, our approach works directly on raw user content across arbitrary platforms. We construct three datasets with known ground-truth data to evaluate our attacks. The first links Hacker News to LinkedIn profiles, using cross-platform references that appear in the profiles. Our second dataset matches users across Reddit movie discussion communities; and the third splits a single user's Reddit history in time to create two pseudonymous profiles to be matched. In each setting, LLM-based methods substantially outperform classical baselines, achieving up to 68% recall at 90% precision compared to near 0% for the best non-LLM method. Our results show that the practical obscurity protecting pseudonymous users online no longer holds and that threat models for online privacy need to be reconsidered.
Simon Lermen, Daniel Paleka, Joshua Swanson, Michael Aerni, Nicholas Carlini, Florian Tramè
#computerscience #cornelluniversity #AiResearch #privacy #anonymity #llm #HackNews #athropic #pseudonymity
#deanonymization -
Large-scale online deanonymization with LLMs
From Cornel University Computer ScienceWe show that large language models can be used to perform at-scale deanonymization. With full Internet access, our agent can re-identify Hacker News users and Anthropic Interviewer participants at high precision, given pseudonymous online profiles and conversations alone, matching what would take hours for a dedicated human investigator. We then design attacks for the closed-world setting. Given two databases of pseudonymous individuals, each containing unstructured text written by or about that individual, we implement a scalable attack pipeline that uses LLMs to: (1) extract identity-relevant features, (2) search for candidate matches via semantic embeddings, and (3) reason over top candidates to verify matches and reduce false positives. Compared to classical deanonymization work (e.g., on the Netflix prize) that required structured data, our approach works directly on raw user content across arbitrary platforms. We construct three datasets with known ground-truth data to evaluate our attacks. The first links Hacker News to LinkedIn profiles, using cross-platform references that appear in the profiles. Our second dataset matches users across Reddit movie discussion communities; and the third splits a single user's Reddit history in time to create two pseudonymous profiles to be matched. In each setting, LLM-based methods substantially outperform classical baselines, achieving up to 68% recall at 90% precision compared to near 0% for the best non-LLM method. Our results show that the practical obscurity protecting pseudonymous users online no longer holds and that threat models for online privacy need to be reconsidered.
Simon Lermen, Daniel Paleka, Joshua Swanson, Michael Aerni, Nicholas Carlini, Florian Tramè
#computerscience #cornelluniversity #AiResearch #privacy #anonymity #llm #HackNews #athropic #pseudonymity
#deanonymization -
https://www.europesays.com/uk/391900/ What your genes can tell you, Afghanistan quake + crying on the job #AfghanistanEarthquake #CryingAtWork #DaveMarchese #DiseaseTests #GeneticDiseaseTesting #Genetics #HackDailyNews #HackNews #HackPodcast #HowToTestForDiseases #Science #ShouldITestMyGenes #ShouldITestMyGenetics #the_latest #TripleJHack #UK #UnitedKingdom
-
What your genes can tell you, Afghanistan quake + crying on the job
If you could find out how you were going to die, would you want to? It’s a question…
#NewsBeep #News #Genetics #afghanistanearthquake #AU #Australia #cryingatwork #davemarchese #diseasetests #geneticdiseasetesting #hackdailynews #hacknews #hackpodcast #howtotestfordiseases #Science #shoulditestmygenes #shoulditestmygenetics #the_latest #triplejhack
https://www.newsbeep.com/au/112958/ -
Why is choral music harder to appreciate?
#HackerNews #choralmusic #appreciation #musicdiscussion #musicappreciation #hacknews #culturalinsights