From ed3903177585884cd0c26793ed24d6b983abe17a Mon Sep 17 00:00:00 2001 From: Ranger Date: Thu, 31 Dec 2020 20:56:30 +0800 Subject: [PATCH] Update Lec 15/16 on IFDS & Soundiness. --- README.assets/image-20201231205814145.png | Bin 0 -> 81424 bytes README.md | 8 +- SUMMARY.md | 13 +- ch0/{README.md => ch0.md} | 0 ch1/ch1.md | 2 +- ch3/context-sensitivity/03-05-cs2.md | 4 +- ch4/04-01-security.md | 2 +- .../image-20201223184349163.png | Bin .../image-20201223184415502.png | Bin .../image-20201223185015690.png | Bin .../image-20201223185231533.png | Bin .../image-20201223185504296.png | Bin .../image-20201223185750701.png | Bin .../image-20201223185957740.png | Bin .../image-20201223190539380.png | Bin .../image-20201223190710495.png | Bin .../image-20201223190916420.png | Bin .../image-20201223191303096.png | Bin .../image-20201223191451507.png | Bin .../image-20201223191716873.png | Bin .../image-20201223192111827.png | Bin .../image-20201223193351919.png | Bin 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z0ifBu3}a85fXsrLiu5a*gf&v=*nhL4i;$D<9v-B2qvWvY$-)GN(jz(70hJM4We)*o zwjb(+=VN;Dqr=)d^^YBBm;Y3L&A7xK>D>FJI2;zWa4oAXssGte!7@owf8LmmYQ}dTetTrDw2;2-X>pk~fPdZB&oT|Gca%gAc|2vnm}? zg23|V=<0gsSnS^E8Q|e8v^3*58(LVWP)AnoiSfD#q5Qu0&Lda-P>FJ(@fD#{37_x3 zQ%pdB9(egWR>X7dR4NpYkR!%EqYvH$!AH`uk3#x4{ru?35fQL+IFv3h<6tAY{rfYi zuATbxFqQ}_{C~gd_WtJ~EtM}$$dN -* [写在前面](ch0/README.md) +* [写在前面](ch0/ch0.md) * [为什么是这本书?](ch0/00-01-why-this-book.md) * [资料来源与版权信息](ch0/00-02-sources-and-license.md) @@ -26,10 +26,13 @@ * [上下文敏感分析(上)](ch3/context-sensitivity/03-04-context-sensitivity.md) * [上下文敏感分析(下)](ch3/context-sensitivity/03-05-cs2.md) -## 指针分析应用 +## 指针分析应用与声明式实现 -* [安全应用——污点分析](ch4/04-01-security.md) +* [应用——污点分析](ch4/04-01-security.md) +* [实现——声明式指针分析](ch4/04-02-Datalog-Based-PA.md) -## 指针分析的另一种样子 +## 其他话题 + +* [另一种静态分析框架——IFDS](ch5/05-01-IFDS.md) +* [从Soundness到Soundiness](ch5/05-02-Soundiness.md) -* [声明式指针分析算法](ch5/05-01-Datalog-Based-PA.md) diff --git a/ch0/README.md b/ch0/ch0.md similarity index 100% rename from ch0/README.md rename to ch0/ch0.md diff --git a/ch1/ch1.md b/ch1/ch1.md index 34e9745..d9c25b9 100644 --- a/ch1/ch1.md +++ b/ch1/ch1.md @@ -21,5 +21,5 @@ * [第五课-数据流分析理论一](https://www.bilibili.com/video/BV1A741117it) * [第六课-数据流分析理论二](https://www.bilibili.com/video/BV1964y1M7nL) -从[这里](https://ranger-nju.gitbook.io/static-program-analysis-book/ch4)直接跳转到施工完毕区域。 +从[这里](https://ranger-nju.gitbook.io/static-program-analysis-book/ch2)直接跳转到施工完毕区域。 diff --git a/ch3/context-sensitivity/03-05-cs2.md b/ch3/context-sensitivity/03-05-cs2.md index 771671b..68e45c3 100644 --- a/ch3/context-sensitivity/03-05-cs2.md +++ b/ch3/context-sensitivity/03-05-cs2.md @@ -47,11 +47,11 @@ 在这一部分,我们只需要理解Select的作用(对于Select的具体实现,会在后面讲解): * ProcessCall接收两个参数,意义是:带有上下文标记的x新增一个带有上下文标记指向目标o。 -* Select接收参数(这里虽然有4个参数,但并非每种实现方式都需要用到所有的4个参数) +* m代表目标方法。 +* Select接收参数(这里虽然有3个参数,但并非每种实现方式都需要用到所有的3个参数) * c。x的上下文标记 * l。调用点本身(call site),在例子中以行号标识调用点 * $$c' : o_i $$。receiver object - * m。 目标method * Select返回callee的context $$ c^t$$ ## Context Sensitivity Variants diff --git a/ch4/04-01-security.md b/ch4/04-01-security.md index 337776c..d7575d7 100644 --- a/ch4/04-01-security.md +++ b/ch4/04-01-security.md @@ -106,7 +106,7 @@ More on Integrity-a Board Definition(在信息流安全以外的语境中,Inte More: -- 通过观察电量消耗、网络流量特征、缓存命中率、服务器相应时长特征,都能以某种方式获得某种程度的机密信息。 +- 通过观察电量消耗、网络流量特征、缓存命中率、服务器响应时长特征,都能以某种方式获得某种程度的机密信息。 - Side Channel: "AF缺乏淡水" diff --git a/ch5/05-01-Datalog-Based-PA.assets/image-20201223184349163.png b/ch4/04-02-Datalog-Based-PA.assets/image-20201223184349163.png similarity index 100% rename from ch5/05-01-Datalog-Based-PA.assets/image-20201223184349163.png rename to ch4/04-02-Datalog-Based-PA.assets/image-20201223184349163.png diff --git a/ch5/05-01-Datalog-Based-PA.assets/image-20201223184415502.png b/ch4/04-02-Datalog-Based-PA.assets/image-20201223184415502.png similarity index 100% rename from ch5/05-01-Datalog-Based-PA.assets/image-20201223184415502.png rename to 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ch5/05-01-Datalog-Based-PA.assets/image-20201223201956852.png rename to ch4/04-02-Datalog-Based-PA.assets/image-20201223201956852.png diff --git a/ch5/05-01-Datalog-Based-PA.assets/image-20201223202140289.png b/ch4/04-02-Datalog-Based-PA.assets/image-20201223202140289.png similarity index 100% rename from ch5/05-01-Datalog-Based-PA.assets/image-20201223202140289.png rename to ch4/04-02-Datalog-Based-PA.assets/image-20201223202140289.png diff --git a/ch5/05-01-Datalog-Based-PA.md b/ch4/04-02-Datalog-Based-PA.md similarity index 76% rename from ch5/05-01-Datalog-Based-PA.md rename to ch4/04-02-Datalog-Based-PA.md index 15964da..269263d 100644 --- a/ch5/05-01-Datalog-Based-PA.md +++ b/ch4/04-02-Datalog-Based-PA.md @@ -13,11 +13,11 @@ Datalog是一种命令式(Declarative)的编程语言。 如果用Imperative的编程方式做指针分析,很麻烦。 - + 而如果用Declarative的方式做编程分析,能够极大地简化实现。 - + # Introduction to Datalog @@ -36,18 +36,18 @@ Datalog是一种命令式(Declarative)的编程语言。 谓词(Predicates)是datalog中的一个主要组成部分,可以看作是数据所组成的一个表(table of data),每一行都代表一个事实(fact)。例如: - + ### Atoms 原子(Atoms)是Datalog中的基本元素,组成和例子如下: - + Atoms可以分成两类 - Relational Atoms - - + - - Arithmetic Atoms - 如`age >= 18` @@ -57,17 +57,17 @@ Atoms可以分成两类 Datalog使用规则来进行推导(inference),其定义如下: - + 当Body中的所有表达式都为True时,Head才为True,如: - + 求解过程(Interpretation of Datalog Rules)——枚举Body中所有关系表达式的可能取值组合,进而得到新的predicate/table。例如: - + - + 谓词分为两类:EDB & IDB。 @@ -78,23 +78,23 @@ Datalog使用规则来进行推导(inference),其定义如下: 例如: - + ### Logic Or 以上例子实际上是逻辑与,而逻辑或则有两种实现方式: - + 此外还需要考虑运算优先级的问题,建议在书写程序时用括号明确地标识期望的运算优先级:`H<-A,(B;C)`。 ### Logic Not/Negation - + ### Recursion - + ## Rule Satety @@ -103,7 +103,7 @@ Datalog使用规则来进行推导(inference),其定义如下: - `A(x) <- B(y), x > y.` - `A(x) <- B(y), !C(x,y).` - + ~~第一次学看不出问题也没问题的~~ @@ -119,13 +119,13 @@ Datalog使用规则来进行推导(inference),其定义如下: `A(x) <- B(x), !A(x)` - + 对应地有第二个准则:**不要把recursion和negation写在同一条规则里**,*即避免写出非A推导出A这样的规则*。 ## Execution of Datalog Program - + Datalog的两大重要特性: @@ -142,7 +142,7 @@ Datalog的两大重要特性: - IDB:指针分析的结果 - Rules:指针分析的规则 - + 和之前一样,我们把Call放到最后处理。 @@ -150,17 +150,17 @@ Datalog的两大重要特性: 我们首先需要对前四条语句建模。输入的EDB代表了4个存储相应类型语句的table,输出为Variable和Field的指向关系。 - + 一个关于EDB的例子如下: - + ## Datalog Rules Body按照红线所示代表前提,Head按照蓝线所示代表结论。 - + ## Example @@ -178,13 +178,13 @@ e = d.f; 结果如下: - + ## PA with Calls 回顾指针分析中Call的规则: - + ### This @@ -192,7 +192,7 @@ e = d.f; (VCall即Virtual Call,ThisVar即This Variable) - + 我们能够同时知道三个信息: @@ -200,49 +200,49 @@ e = d.f; 2. 方法m是可达的 3. 方法m可达是因为在l行处存在对方法m的调用 - + ### Parameters 接下来要处理参数的传递,与之前类似,引入EDB标识Argument(调用语句行号,参数标号和参数本身)和Parameter(被调用方法,参数标号和参数本身): - + 而对应用Datalog书写的规则如果用自然语言描述,就是处理行号l处对m的调用时,根据形参和实参的信息,将实参已经有的指向关系传递给形参数。 - + ### Return Value 引入EDB: - + 对应的Datalog Rule,处理返回值的指向关系: - + ### Sum up 以上三个部分总结起来,就能得到以下的Datalog Rules。 - + 进而能得到全程序的分析算法如下。值得一提的是,在VarPointsTo规则中,添加Reachable(m)跳过不可达方法中的对象。而其他规则不需要加这一条件,则是因为它们都有VarPointsTo规则作为Body的一部分。 - + # Taint Analysis via Datalog ## Datalog Model -同样需要用户提供Source和Sink。输出被标记的数据可能流到的Sink方法。 +同样需要用户提供Source和Sink。输出被标记的数据可能流到的Sink方法。 ## Datalog Rules 说明:这里参数列表中用`—`表示通配符,即不关心枚举时这个位置取什么值。 - + # Key Points diff --git a/ch4/ch4.md b/ch4/ch4.md index fd3c1f3..db316fb 100644 --- a/ch4/ch4.md +++ b/ch4/ch4.md @@ -1,3 +1,6 @@ -# 指针分析应用 +# 指针分析应用、实现与其他 -接下来说说基于指针分析的污点分析(Taint Analysis)。 +接下来说说基于指针分析的静态分析相关技术知识: + +- 安全应用方向的污点分析(Taint Analysis)。 +- 如何用声明式的语言Datalog实现指针分析。 diff --git a/ch5/05-01-IFDS.assets/image-20201224200732868.png b/ch5/05-01-IFDS.assets/image-20201224200732868.png new file mode 100644 index 0000000000000000000000000000000000000000..481b7d59f1e90bedbf6269a9debc12db21539ae0 GIT binary patch literal 505736 zcmeFZ_dnPB|3B`Wb{a&8Bq}Q-qhuG7k&(y>Ws?zQuTDj>%1&1Hp4l@MGRp{=6|%{Q z?9cUaUhnti@_zjX-ygnRUYFP997jB!_i?*j*X{94S@G(@{j~c@NJtLK%1Eh@knCY1 zA=z25cQ?Mmap2Pd5|Wc7vQn2-9iyfP98@XQ*Z2G~nUGPxq$)vqG|zVz9s9DxbY<)3l3qR20J*JUl$K=V|@VnCs{6Bu(uh^{G_Od?+)wxwZA{+hN1m>FHUE_j6-W zqF2XS#TGe)`GomCHCOJhqdA3N`G5W#ix?OF>;L>a2??u*viHva`pVZhvVH&cg?vfI zBmeaU!>1Cb|LY4M4|&S`*B4&U?+^U1FPs#nWcsgPe94|w>hJ&iEf1&v{|EX1%8D4T 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