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LangChain CVE-2025-68664: Jinsi PII Inavyovuja Kupitia Mtiririko Wako wa RAG

CVSS 9.3. Kazi za ushirikiano wa LangChain hufichua vigeuzi vya mazingira na siri kwa LLM zinazodhibitiwa na washambuliaji. Jinsi ya kugundua na kurekebisha uvujaji wa PII.

March 16, 20268 dakika kusoma
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LangChain CVE-2025-68664: Jinsi PII Inavyovuja Kupitia Mtiririko Wako wa RAG

Imesasishwa kwa 2026.

Kasoro muhimu ilipatikana katika LangChain mwishoni mwa 2025. CVE ni CVE-2025-68664. Alama ya CVSS ni 9.3 (Muhimu).

Inalenga msimbo wa ushirikiano wa LangChain.

CVE-2025-68664 Inafanya Nini

LangChain ina kazi mbili za ushirikiano: `dumps()` na `dumpd()`. Zinabadilisha vitu vya Python kuwa maandishi.

Kasoro iko katika usimamizi wa kufunga.

LangChain inapoushirikisha inayoweza kuita, inakamata muktadha wa kufunga.

Mshambuliaji anayedhibiti jibu la LLM anaweza kusababisha `dumps()`. Kazi hiyo kisha inasoma vigeuzi vya mazingira kutoka kwa mchakato wa Python.

Matokeo ni ufichuzi wa data. Funguo za API, nyuzi za hifadhidata, siri za JWT, na vitambulisho vya AWS vinaweza kuonekana katika matokeo ya mfano.

Mshambuliaji anayeingiza maandishi kwenye hati ya chanzo cha RAG anaweza kusoma siri zako za uzalishaji.

Toleo linaloathiriwa: LangChain chini ya 0.3.22 (Python). Toleo 0.3.22 lina marekebisho.

Data ya PyPI inaonyesha matumizi mapana ya matoleo ya zamani hadi Machi 2026.

Jinsi PII Inavyovuja Katika Mtiririko wa RAG

CVE-2025-68664 ni ya kushangaza. Lakini ni kesi moja tu ya tatizo pana zaidi.

Data huvuja kupitia mtiririko wa RAG mara kwa mara. Hakuna mshambuliaji anayehitajika.

Hapa kuna usanidi wa kawaida wa RAG wa biashara.

Kwanza, uingizaji. Unaorodhesha hati za kampuni kwenye hifadhi ya vektori. Fikiria tiketi za msaada, barua pepe za wateja, mikataba, na rekodi za HR.

Hifadhi za kawaida za vektori ni Pinecone, Weaviate, na pgvector.

Ifuatayo, urejeshaji. Mtumiaji anauliza swali. Mfumo unachomoa vipande vitano muhimu zaidi kutoka kwenye hifadhi.

Kisha, uzalishaji. Vipande hivyo vinaenda kwa LLM — GPT-4o, Claude, au Gemini — kama muktadha.

Hatua ya pili ndiyo tatizo. Vipande vilivyorejeshwa vina chochote kilichokuwa katika hati za chanzo. Hiyo inajumuisha:

  • Majina ya wateja, anwani za barua pepe, na nambari za simu
  • Thamani za mikataba, nambari za akaunti, na vitambulisho vya kodi
  • Data ya mshahara wa wafanyakazi na maelezo ya tathmini ya utendaji
  • Majina ya wagonjwa katika maelezo ya kliniki
  • Nambari za kitambulisho cha kitaifa katika faili za uhamiaji

Data hiyo inaenda kwa LLM kama ilivyo. Inaweza kuonekana katika matokeo ya mfano.

Inaorodheshwa na mtoa huduma wa LLM. Inakaa katika historia yako ya mazungumzo. Inatiririka kwenye steki yako ya uangalizi.

Hakuna shambulio linalohitajika. Hivi ndivyo RAG inavyofanya kazi kwa muundo. Muundo huo unaleta hatari ya kweli ya faragha.

Mifumo 68 ya Siri katika Hifadhi za Hati za Biashara

Zana za usalama zinafuatilia mifumo 68 ya siri inayojulikana. Inaonekana mara nyingi zaidi kuliko timu zinavyotarajiwa.

Hapa kuna zile za kawaida zaidi.

  • AWS Access Key IDs (`AKIA...`)
  • Funguo za API za OpenAI (`sk-...`)
  • Funguo za API za Anthropic (`sk-ant-...`)
  • URI za hifadhidata (`postgresql://mtumiaji:nenosiri@mwenyeji/db`)
  • Tokeni za JWT (vichwa vilivyosimbishwa kwa base64)
  • Tokeni za Ufikiaji wa Kibinafsi za GitHub
  • Funguo za siri za Stripe (`sk_live_...`)
  • Funguo za API za SendGrid
  • SID za akaunti za Twilio na tokeni za uthibitishaji
  • Vizuizi vya PEM vya ufunguo wa kibinafsi

Tiketi ya msaada inaweza kuhifadhi ufunguo wa API wa mteja kutoka kwenye kikao cha utatuzi.

Mkataba unaweza kujumuisha vitambulisho vya hifadhidata kutoka handover ya kiufundi.

Faili ya usanidi iliyoorodheshwa kwa bahati mbaya inaweza kufichua hifadhi nzima ya siri.

Faili hizi zinapoingia kwenye hifadhi ya vektori bila usafi, kila swali linaweza kupeleka siri kwa LLM.

Zinaweza kufikia mtumiaji wa mwisho pia.

Rekebisha: Futa Utambulisho Kabla ya Kuweka

Mkabala sahihi unafuta utambulisho wa hati kabla ya kugawanya na kuweka.

Hatua hii inahitajika kwa mfumo wowote unaoshughulikia data ya wateja.

Hapa kuna mfano wa Python ukitumia API ya anonym.legal:

```python import requests import os

ANONYM_API_KEY = os.environ["ANONYM_API_KEY"] ANONYM_BASE_URL = "https://anonym.legal/api"

def anonymize_before_embedding(text: str) -> tuple[str, dict]: """Futa utambulisho wa PII kabla ya kuweka.""" response = requests.post( f"{ANONYM_BASE_URL}/presidio/anonymize", json={ "text": text, "language": "en", "anonymizers": { "DEFAULT": {"type": "replace", "new_value": "[REDACTED]"}, "PERSON": {"type": "mask", "masking_char": "*", "chars_to_mask": 4, "from_end": False}, "EMAIL_ADDRESS": {"type": "replace", "new_value": "[EMAIL]"}, "PHONE_NUMBER": {"type": "replace", "new_value": "[PHONE]"}, "CRYPTO": {"type": "replace", "new_value": "[SECRET]"}, "URL": {"type": "keep"}, } }, headers={"Authorization": f"Bearer {ANONYM_API_KEY}"} ) result = response.json() return result["text"], result.get("items", [])

def build_rag_index(documents: list[str], vectorstore): """Jenga faharasa ya RAG yenye hati safi peke yake.""" anonymized_docs = [] for doc in documents: clean_text, entities = anonymize_before_embedding(doc) anonymized_docs.append(clean_text) print(f"Vipengele {len(entities)} vya PII vimeondolewa kutoka kwenye hati") vectorstore.add_texts(anonymized_docs) ```

API ya anonym.legal inashughulikia aina 285+ za vipengele. Majina, barua pepe, nambari za simu, vitambulisho vya kitaifa, funguo za API, na URI za hifadhidata zinashikwa.

Hakuna kitu chenye siri kinachofika kwenye hifadhi ya vektori. Kwa hivyo hakuna kitu chenye siri kinachoweza kuvuja kwa watumiaji.

Angalia mwongozo wa msanidi programu kwa mifumo ya usanidi wa LangChain na LlamaIndex.

Rekebisha CVE-2025-68664 Sasa Hivi

Ukiendesha LangChain chini ya 0.3.22, sasisha sasa:

```bash pip install "langchain>=0.3.22" "langchain-core>=0.3.22" ```

Baada ya kurekebisha, angalia usanidi wa mnyororo wako kwa hatari ya sindano. Hapa kuna hatua tatu za kuchukua.

Kwanza, thibitisha vipande vilivyorejeshwa. Fanya hivi kabla havijafika kwa LLM.

Ondoa maudhui yanayolingana na mifumo ya sindano kama vile `puuza maagizo ya awali`, `mfumo:`, au `<INST>`.

Pili, futa utambulisho kabla ya kuweka. Hii inapunguza eneo la shambulio.

Sindano ikitokea, data nyeti haipo pale ili kuitoa.

Tatu, zuia ruhusa za mnyororo. Minyororo ya LangChain haipaswi kusoma vigeuzi vya mazingira zaidi ya vinavyohitajika.

Tumia akaunti ya huduma yenye upeo mdogo.

Hesabu ni Rahisi

Alama ya CVSS ni 9.3. Marekebisho ni wito mmoja wa API kwa kila hati.

Mchanganyiko wa CVE-2025-68664 na hatari ya jumla ya data ya RAG ni dhima ya kweli.

Suluhisho ni wazi: futa utambulisho wakati wa uingizaji, si wakati wa swali.

Angalia muhtasari wa usalama na uzingatiaji kwa mahitaji ya RAG ya biashara.

Vyanzo

  • NVD CVE-2025-68664, CVSS 9.3, udhaifu wa ushirikiano wa LangChain
  • Ushauri wa usalama wa LangChain, langchain-ai/langchain GitHub, 2025
  • OWASP LLM Top 10: LLM01 Sindano ya Maombi, LLM06 Ufichuzi wa Maelezo Nyeti
  • Hati ya aina za vipengele za anonym.legal — aina 285+ zinazoungwa mkono

Tayari kulinda data yako?

Anza kuanonymisha PII na aina 285+ za vitu katika lugha 48.

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We update this page when our platform or the law changes.

Read our founder note for how we work.

Each change shows up in the timestamp at the top.

Related reading

We follow these rules

  • GDPR (EU 2016/679).
  • ISO/IEC 27001:2022.
  • NIS2 (EU 2022/2555).
  • HIPAA safe harbor under 45 CFR § 164.514(b)(2).

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We do not sell your data.

We do not train models on your text.

We store your files in Germany.

You can delete your account at any time.

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Our servers live in Falkenstein, Germany.

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We reply within one business day.

How we test

We run a full check suite on every release.

Each surface gets its own sweep script and report.

Human reviewers spot-check the output each week.

We track recall and precision on a labelled set.

Bad runs block the deploy.

What we never do

  • We never sell your information to third parties.
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One credit covers one short job.

Long jobs use a few credits each.

You can top up at any time.

Unused credits roll over each month.

Read the plans page for current rates.

Who built this

A small team of engineers and lawyers built this.

We ship from Europe and work in the open.

Our founder note spells out why we started.

Where to start

How the parts fit

A browser add-on cleans text inside Chrome.

A Word plug-in handles drafts in Office.

A small desktop tool works on whole folders.

An agent protocol link feeds large models safely.

All four share one core engine and one rule set.

Words from our team

We started this work after a lunch about cookies.

One friend kept getting odd ads on her phone.

We asked why a court file leaked through a draft.

We sketched the first build on a napkin that week.

By month three we had a tiny demo for a friend.

She used it on her first case the next day.

Common questions we hear

Can the tool read scanned PDFs? Yes, with OCR.

Does it work on long files? Yes, in small chunks.

Can I roll my own rule set? Yes, save it as a preset.

Does it run offline? The desktop build runs offline.

Do you keep my files? No, the cloud build wipes after each run.

Will it learn from my work? No, we never train on inputs.

A short tour of the workflow

Upload a file or paste a snippet of prose.

Pick the entities you want gone from the draft.

Choose a method: replace, mask, hash, encrypt, or redact.

Press run and watch the side panel show each hit.

Skim the result and tweak any rule that misfired.

Save the cleaned file or send it to a teammate.